List of datasets in computer vision and image processing explained

This is a list of datasets for machine learning research. It is part of the list of datasets for machine-learning research. These datasets consist primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification.

Object detection and recognition

Dataset Name	Brief description	Preprocessing	Instances	Format	Default Task	Created (updated)	Reference	Creator
Ego 4D	A massive-scale, egocentric dataset and benchmark suite collected across 74 worldwide locations and 9 countries, with over 3,670 hours of daily-life activity video.	Object bounding boxes, transcriptions, labeling.	3,670 video hours	video, audio, transcriptions	Multimodal first-person task	2022	^[1]	K. Grauman et al.
Visual Genome	Images and their description		108,000	images, text	Image captioning	2016	^[2]	R. Krishna et al.
Berkeley 3-D Object Dataset	849 images taken in 75 different scenes. About 50 different object classes are labeled.	Object bounding boxes and labeling.	849	labeled images, text	Object recognition	2014	^[3] ^[4]	A. Janoch et al.
Berkeley Segmentation Data Set and Benchmarks 500 (BSDS500)	500 natural images, explicitly separated into disjoint train, validation and test subsets + benchmarking code. Based on BSDS300.	Each image segmented by five different subjects on average.	500	Segmented images	Contour detection and hierarchical image segmentation	2011	^[5]	University of California, Berkeley
Microsoft Common Objects in Context (COCO)	complex everyday scenes of common objects in their natural context.	Object highlighting, labeling, and classification into 91 object types.	2,500,000	Labeled images, text	Object recognition	2015	^[6] ^[7] ^[8]	T. Lin et al.
SUN Database	Very large scene and object recognition database.	Places and objects are labeled. Objects are segmented.	131,067	Images, text	Object recognition, scene recognition	2014	^[9] ^[10]	J. Xiao et al.
ImageNet	Labeled object image database, used in the ImageNet Large Scale Visual Recognition Challenge	Labeled objects, bounding boxes, descriptive words, SIFT features	14,197,122	Images, text	Object recognition, scene recognition	2009 (2014)	^[11] ^[12]	J. Deng et al.
Open Images	A Large set of images listed as having CC BY 2.0 license with image-level labels and bounding boxes spanning thousands of classes.	Image-level labels, Bounding boxes	9,178,275	Images, text	Classification, Object recognition	2017 (V7 : 2022)	^[13]
TV News Channel Commercial Detection Dataset	TV commercials and news broadcasts.	Audio and video features extracted from still images.	129,685	Text	Clustering, classification	2015	^[14] ^[15]	P. Guha et al.
Statlog (Image Segmentation) Dataset	The instances were drawn randomly from a database of 7 outdoor images and hand-segmented to create a classification for every pixel.	Many features calculated.	2310	Text	Classification	1990	^[16]	University of Massachusetts
Caltech 101	Pictures of objects.	Detailed object outlines marked.	9146	Images	Classification, object recognition	2003	^[17] ^[18]	F. Li et al.
Caltech-256	Large dataset of images for object classification.	Images categorized and hand-sorted.	30,607	Images, Text	Classification, object detection	2007	^[19] ^[20]	G. Griffin et al.
COYO-700M	Image–text-pair dataset	10 billion pairs of alt-text and image sources in HTML documents in CommonCrawl	746,972,269	Images, Text	Classification, Image-Language	2022
SIFT10M Dataset	SIFT features of Caltech-256 dataset.	Extensive SIFT feature extraction.	11,164,866	Text	Classification, object detection	2016	^[21]	X. Fu et al.
LabelMe	Annotated pictures of scenes.	Objects outlined.	187,240	Images, text	Classification, object detection	2005	^[22]	MIT Computer Science and Artificial Intelligence Laboratory
PASCAL VOC Dataset	Large number of images for classification tasks.	Labeling, bounding box included	500,000	Images, text	Classification, object detection	2010	^[23] ^[24]	M. Everingham et al.
CIFAR-10 Dataset	Many small, low-resolution, images of 10 classes of objects.	Classes labelled, training set splits created.	60,000	Images	Classification	2009	^[25] ^[26]	A. Krizhevsky et al.
CIFAR-100 Dataset	Like CIFAR-10, above, but 100 classes of objects are given.	Classes labelled, training set splits created.	60,000	Images	Classification	2009		A. Krizhevsky et al.
CINIC-10 Dataset	A unified contribution of CIFAR-10 and Imagenet with 10 classes, and 3 splits. Larger than CIFAR-10.	Classes labelled, training, validation, test set splits created.	270,000	Images	Classification	2018	^[27]	Luke N. Darlow, Elliot J. Crowley, Antreas Antoniou, Amos J. Storkey
Fashion-MNIST	A MNIST-like fashion product database	Classes labelled, training set splits created.	60,000	Images	Classification	2017		Zalando SE
notMNIST	Some publicly available fonts and extracted glyphs from them to make a dataset similar to MNIST. There are 10 classes, with letters A–J taken from different fonts.	Classes labelled, training set splits created.	500,000	Images	Classification	2011	^[28]	Yaroslav Bulatov
Linnaeus 5 dataset	Images of 5 classes of objects.	Classes labelled, training set splits created.	8000	Images	Classification	2017	^[29]	Chaladze & Kalatozishvili
11K Hands	11,076 hand images (1600 x 1200 pixels) of 190 subjects, of varying ages between 18 – 75 years old, for gender recognition and biometric identification.	None	11,076 hand images	Images and (.mat, .txt, and .csv) label files	Gender recognition and biometric identification	2017	^[30]	M Afifi
CORe50	Specifically designed for Continuous/Lifelong Learning and Object Recognition, is a collection of more than 500 videos (30fps) of 50 domestic objects belonging to 10 different categories.	Classes labelled, training set splits created based on a 3-way, multi-runs benchmark.	164,866 RBG-D images	images (.png or .pkl)and (.pkl, .txt, .tsv) label files	Classification, Object recognition	2017	^[31]	V. Lomonaco and D. Maltoni
OpenLORIS-Object	Lifelong/Continual Robotic Vision dataset (OpenLORIS-Object) collected by real robots mounted with multiple high-resolution sensors, includes a collection of 121 object instances (1st version of dataset, 40 categories daily necessities objects under 20 scenes). The dataset has rigorously considered 4 environment factors under different scenes, including illumination, occlusion, object pixel size and clutter, and defines the difficulty levels of each factor explicitly.	Classes labelled, training/validation/testing set splits created by benchmark scripts.	1,106,424 RBG-D images	images (.png and .pkl)and (.pkl) label files	Classification, Lifelong object recognition, Robotic Vision	2019	^[32]	Q. She et al.
THz and thermal video data set	This multispectral data set includes terahertz, thermal, visual, near infrared, and three-dimensional videos of objects hidden under people's clothes.	3D lookup tables are provided that allow you to project images onto 3D point clouds.	More than 20 videos. The duration of each video is about 85 seconds (about 345 frames).	AP2J	Experiments with hidden object detection	2019	^[33] ^[34]	Alexei A. Morozov and Olga S. Sushkova

Object detection and recognition for autonomous vehicles

Dataset Name	Brief description	Preprocessing	Instances	Format	Default Task	Created (updated)	Reference	Creator
Cityscapes Dataset	Stereo video sequences recorded in street scenes, with pixel-level annotations. Metadata also included.	Pixel-level segmentation and labeling	25,000	Images, text	Classification, object detection	2016	^[35]	Daimler AG et al.
German Traffic Sign Detection Benchmark Dataset	Images from vehicles of traffic signs on German roads. These signs comply with UN standards and therefore are the same as in other countries.	Signs manually labeled	900	Images	Classification	2013	^[36] ^[37]	S. Houben et al.
KITTI Vision Benchmark Dataset	Autonomous vehicles driving through a mid-size city captured images of various areas using cameras and laser scanners.	Many benchmarks extracted from data.	>100 GB of data	Images, text	Classification, object detection	2012	^[38] ^[39]	A. Geiger et al.
FieldSAFE	Multi-modal dataset for obstacle detection in agriculture including stereo camera, thermal camera, web camera, 360-degree camera, lidar, radar, and precise localization.	Classes labelled geographically.	>400 GB of data	Images and 3D point clouds	Classification, object detection, object localization	2017	^[40]	M. Kragh et al.
Daimler Monocular Pedestrian Detection dataset	It is a dataset of pedestrians in urban environments.	Pedestrians are box-wise labeled.	Labeled part contains 15560 samples with pedestrians and 6744 samples without. Test set contains 21790 images without labels.	Images	Object recognition and classification	2006	^[41] ^[42] ^[43]	Daimler AG
CamVid	The Cambridge-driving Labeled Video Database (CamVid) is a collection of videos.	The dataset is labeled with semantic labels for 32 semantic classes.	over 700 images	Images	Object recognition and classification	2008	^[44] ^[45] ^[46]	Gabriel J. Brostow, Jamie Shotton, Julien Fauqueur, Roberto Cipolla
RailSem19	RailSem19 is a dataset for understanding scenes for vision systems on railways.	The dataset is labeled semanticly and box-wise.	8500	Images	Object recognition and classification, scene recognition	2019	^[47] ^[48]	Oliver Zendel, Markus Murschitz, Marcel Zeilinger, Daniel Steininger, Sara Abbasi, Csaba Beleznai
BOREAS	BOREAS is a multi-season autonomous driving dataset. It includes data from includes a Velodyne Alpha-Prime (128-beam) lidar, a FLIR Blackfly S camera, a Navtech CIR304-H radar, and an Applanix POS LV GNSS-INS.	The data is annotated by 3D bounding boxes.	350 km of driving data	Images, Lidar and Radar data	Object recognition and classification, scene recognition	2023	^[49] ^[50]	Keenan Burnett, David J. Yoon, Yuchen Wu, Andrew Zou Li, Haowei Zhang, Shichen Lu, Jingxing Qian, Wei-Kang Tseng, Andrew Lambert, Keith Y.K. Leung, Angela P. Schoellig, Timothy D. Barfoot
Bosch Small Traffic Lights Dataset	It is a dataset of traffic lights.	The labeling include bounding boxes of traffic lights together with their state (active light).	5000 images for training and a video sequence of 8334 frames for evaluation	Images	Traffic light recognition	2017	^[51] ^[52]	Karsten Behrendt, Libor Novak, Rami Botros
FRSign	It is a dataset of French railway signals.	The labeling include bounding boxes of railway signals together with their state (active light).	more than 100000	Images	Railway signal recognition	2020	^[53] ^[54]	Jeanine Harb, Nicolas Rébéna, Raphaël Chosidow, Grégoire Roblin, Roman Potarusov, Hatem Hajri
GERALD	It is a dataset of German railway signals.	The labeling include bounding boxes of railway signals together with their state (active light).	5000	Images	Railway signal recognition	2023	^[55] ^[56]	Philipp Leibner, Fabian Hampel, Christian Schindler
Multi-cue pedestrian	Multi-cue onboard pedestrian detection dataset is a dataset for detection of pedestrians.	The databaset is labeled box-wise.	1092 image pairs with 1776 boxes for pedestrians	Images	Object recognition and classification	2009	^[57]	Christian Wojek, Stefan Walk, Bernt Schiele
RAWPED	RAWPED is a dataset for detection of pedestrians in the context of railways.	The dataset is labeled box-wise.	26000	Images	Object recognition and classification	2020	^[58] ^[59]	Tugce Toprak, Burak Belenlioglu, Burak Aydın, Cuneyt Guzelis, M. Alper Selver
OSDaR23	OSDaR23 is a multi-sensory dataset for detection of objects in the context of railways.	The databaset is labeled box-wise.	16874 frames	Images, Lidar, Radar and Infrared	Object recognition and classification	2023	^[60] ^[61]	DZSF, Digitale Schiene Deutschland, and FusionSystems
Agroverse	Argoverse is a multi-sensory dataset for detection of objects in the context of roads.	The dataset is annotated box-wise.	320 hours of recording	Data from 7 cameras and LiDAR	Object recognition and classification, object tracking	2022	^[62] ^[63]	Argo AI, Carnegie Mellon University, Georgia Institute of Technology

Facial recognition

In computer vision, face images have been used extensively to develop facial recognition systems, face detection, and many other projects that use images of faces.

Dataset name	Brief description	Preprocessing	Instances	Format	Default task	Created (updated)	Reference	Creator
Aff-Wild	298 videos of 200 individuals, ~1,250,000 manually annotated images: annotated in terms of dimensional affect (valence-arousal); in-the-wild setting; color database; various resolutions (average = 640x360)	the detected faces, facial landmarks and valence-arousal annotations	~1,250,000 manually annotated images	video (visual + audio modalities)	affect recognition (valence-arousal estimation)	2017	CVPR^[64] IJCV^[65]	D. Kollias et al.
Aff-Wild2	558 videos of 458 individuals, ~2,800,000 manually annotated images: annotated in terms of i) categorical affect (7 basic expressions: neutral, happiness, sadness, surprise, fear, disgust, anger); ii) dimensional affect (valence-arousal); iii) action units (AUs 1,2,4,6,12,15,20,25); in-the-wild setting; color database; various resolutions (average = 1030x630)	the detected faces, detected and aligned faces and annotations	~2,800,000 manually annotated images	video (visual + audio modalities)	affect recognition (valence-arousal estimation, basic expression classification, action unit detection)	2019	BMVC^[66] FG^[67]	D. Kollias et al.
FERET (facial recognition technology)	11338 images of 1199 individuals in different positions and at different times.	None.	11,338	Images	Classification, face recognition	2003	^[68] ^[69]	United States Department of Defense
Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)	7,356 video and audio recordings of 24 professional actors. 8 emotions each at two intensities.	Files labelled with expression. Perceptual validation ratings provided by 319 raters.	7,356	Video, sound files	Classification, face recognition, voice recognition	2018	^[70] ^[71]	S.R. Livingstone and F.A. Russo
SCFace	Color images of faces at various angles.	Location of facial features extracted. Coordinates of features given.	4,160	Images, text	Classification, face recognition	2011	^[72] ^[73]	M. Grgic et al.
Yale Face Database	Faces of 15 individuals in 11 different expressions.	Labels of expressions.	165	Images	Face recognition	1997	^[74] ^[75]	J. Yang et al.
Cohn-Kanade AU-Coded Expression Database	Large database of images with labels for expressions.	Tracking of certain facial features.	500+ sequences	Images, text	Facial expression analysis	2000	^[76] ^[77]	T. Kanade et al.
JAFFE Facial Expression Database	213 images of 7 facial expressions (6 basic facial expressions + 1 neutral) posed by 10 Japanese female models.	Images are cropped to the facial region. Includes semantic ratings data on emotion labels.	213	Images, text	Facial expression cognition	1998	^[78] ^[79]	Lyons, Kamachi, Gyoba
FaceScrub	Images of public figures scrubbed from image searching.	Name and m/f annotation.	107,818	Images, text	Face recognition	2014	^[80] ^[81]	H. Ng et al.
BioID Face Database	Images of faces with eye positions marked.	Manually set eye positions.	1521	Images, text	Face recognition	2001	^[82] ^[83]	BioID
Skin Segmentation Dataset	Randomly sampled color values from face images.	B, G, R, values extracted.	245,057	Text	Segmentation, classification	2012	^[84] ^[85]	R. Bhatt.
Bosphorus	3D Face image database.	34 action units and 6 expressions labeled; 24 facial landmarks labeled.	4652	Images, text	Face recognition, classification	2008	^[86] ^[87]	A Savran et al.
UOY 3D-Face	neutral face, 5 expressions: anger, happiness, sadness, eyes closed, eyebrows raised.	labeling.	5250	Images, text	Face recognition, classification	2004	^[88] ^[89]	University of York
CASIA 3D Face Database	Expressions: Anger, smile, laugh, surprise, closed eyes.	None.	4624	Images, text	Face recognition, classification	2007	^[90] ^[91]	Institute of Automation, Chinese Academy of Sciences
CASIA NIR	Expressions: Anger Disgust Fear Happiness Sadness Surprise	None.	480	Annotated Visible Spectrum and Near Infrared Video captures at 25 frames per second	Face recognition, classification	2011	^[92]	Zhao, G. et al.
BU-3DFE	neutral face, and 6 expressions: anger, happiness, sadness, surprise, disgust, fear (4 levels). 3D images extracted.	None.	2500	Images, text	Facial expression recognition, classification	2006	^[93]	Binghamton University
Face Recognition Grand Challenge Dataset	Up to 22 samples for each subject. Expressions: anger, happiness, sadness, surprise, disgust, puffy. 3D Data.	None.	4007	Images, text	Face recognition, classification	2004	^[94] ^[95]	National Institute of Standards and Technology
Gavabdb	Up to 61 samples for each subject. Expressions neutral face, smile, frontal accentuated laugh, frontal random gesture. 3D images.	None.	549	Images, text	Face recognition, classification	2008	^[96] ^[97]	King Juan Carlos University
3D-RMA	Up to 100 subjects, expressions mostly neutral. Several poses as well.	None.	9971	Images, text	Face recognition, classification	2004	^[98] ^[99]	Royal Military Academy (Belgium)
SoF	112 persons (66 males and 46 females) wear glasses under different illumination conditions.	A set of synthetic filters (blur, occlusions, noise, and posterization) with different level of difficulty.	42,592 (2,662 original image × 16 synthetic image)	Images, Mat file	Gender classification, face detection, face recognition, age estimation, and glasses detection	2017	^[100] ^[101]	Afifi, M. et al.
IMDb-WIKI	IMDb and Wikipedia face images with gender and age labels.	None	523,051	Images	Gender classification, face detection, face recognition, age estimation	2015	^[102]	R. Rothe, R. Timofte, L. V. Gool

Action recognition

Dataset name	Brief description	Preprocessing	Instances	Format	Default Task	Created (updated)	Reference	Creator
TV Human Interaction Dataset	Videos from 20 different TV shows for prediction social actions: handshake, high five, hug, kiss and none.	None.	6,766 video clips	video clips	Action prediction	2013	^[103]	Patron-Perez, A. et al.
Berkeley Multimodal Human Action Database (MHAD)	Recordings of a single person performing 12 actions	MoCap pre-processing	660 action samples	8 PhaseSpace Motion Capture, 2 Stereo Cameras, 4 Quad Cameras, 6 accelerometers, 4 microphones	Action classification	2013	^[104]	Ofli, F. et al.
THUMOS Dataset	Large video dataset for action classification.	Actions classified and labeled.	45M frames of video	Video, images, text	Classification, action detection	2013	^[105] ^[106]	Y. Jiang et al.
MEXAction2	Video dataset for action localization and spotting	Actions classified and labeled.	1000	Video	Action detection	2014	^[107]	Stoian et al.

Handwriting and character recognition

Dataset name	Brief description	Preprocessing	Instances	Format	Default Task	Created (updated)	Reference	Creator
Artificial Characters Dataset	Artificially generated data describing the structure of 10 capital English letters.	Coordinates of lines drawn given as integers. Various other features.	6000	Text	Handwriting recognition, classification	1992	^[108]	H. Guvenir et al.
Letter Dataset	Upper-case printed letters.	17 features are extracted from all images.	20,000	Text	OCR, classification	1991	^[109] ^[110]	D. Slate et al.
CASIA-HWDB	Offline handwritten Chinese character database. 3755 classes in the GB 2312 character set.	Gray-scaled images with background pixels labeled as 255.	1,172,907	Images, Text	Handwriting recognition, classification	2009	^[111]	CASIA
CASIA-OLHWDB	Online handwritten Chinese character database, collected using Anoto pen on paper. 3755 classes in the GB 2312 character set.	Provides the sequences of coordinates of strokes.	1,174,364	Images, Text	Handwriting recognition, classification	2009	^[112]	CASIA
Character Trajectories Dataset	Labeled samples of pen tip trajectories for people writing simple characters.	3-dimensional pen tip velocity trajectory matrix for each sample	2858	Text	Handwriting recognition, classification	2008	^[113] ^[114]	B. Williams
Chars74K Dataset	Character recognition in natural images of symbols used in both English and Kannada		74,107		Character recognition, handwriting recognition, OCR, classification	2009	^[115]	T. de Campos
EMNIST dataset	Handwritten characters from 3600 contributors	Derived from NIST Special Database 19. Converted to 28x28 pixel images, matching the MNIST dataset.^[116]	800,000	Images	character recognition, classification, handwriting recognition	2016	EMNIST dataset^[117] Documentation^[118]	Gregory Cohen, et al.
UJI Pen Characters Dataset	Isolated handwritten characters	Coordinates of pen position as characters were written given.	11,640	Text	Handwriting recognition, classification	2009	^[119] ^[120]	F. Prat et al.
Gisette Dataset	Handwriting samples from the often-confused 4 and 9 characters.	Features extracted from images, split into train/test, handwriting images size-normalized.	13,500	Images, text	Handwriting recognition, classification	2003	^[121]	Yann LeCun et al.
Omniglot dataset	1623 different handwritten characters from 50 different alphabets.	Hand-labeled.	38,300	Images, text, strokes	Classification, one-shot learning	2015	^[122]	American Association for the Advancement of Science
MNIST database	Database of handwritten digits.	Hand-labeled.	60,000	Images, text	Classification	1994	^[123] ^[124]	National Institute of Standards and Technology
Optical Recognition of Handwritten Digits Dataset	Normalized bitmaps of handwritten data.	Size normalized and mapped to bitmaps.	5620	Images, text	Handwriting recognition, classification	1998	^[125]	E. Alpaydin et al.
Pen-Based Recognition of Handwritten Digits Dataset	Handwritten digits on electronic pen-tablet.	Feature vectors extracted to be uniformly spaced.	10,992	Images, text	Handwriting recognition, classification	1998	^[126] ^[127]	E. Alpaydin et al.
Semeion Handwritten Digit Dataset	Handwritten digits from 80 people.	All handwritten digits have been normalized for size and mapped to the same grid.	1593	Images, text	Handwriting recognition, classification	2008	^[128]	T. Srl
HASYv2	Handwritten mathematical symbols	All symbols are centered and of size 32px x 32px.	168233	Images, text	Classification	2017	^[129]	Martin Thoma
Noisy Handwritten Bangla Dataset	Includes Handwritten Numeral Dataset (10 classes) and Basic Character Dataset (50 classes), each dataset has three types of noise: white gaussian, motion blur, and reduced contrast.	All images are centered and of size 32x32.	Numeral Dataset: 23330, Character Dataset: 76000	Images,text	Handwriting recognition,classification	2017	^[130]	M. Karki et al.

Aerial images

Dataset name	Brief description	Preprocessing	Instances	Format	Default Task	Created (updated)	Reference	Creator
iSAID: Instance Segmentation in Aerial Images Dataset		Precise instance-level annotatio carried out by professional annotators, cross-checked and validated by expert annotators complying with well-defined guidelines.	655,451 (15 classes)	Images, jpg, json	Aerial Classification, Object Detection, Instance Segmentation	2019	^[131] ^[132]	Syed Waqas Zamir,Aditya Arora, Akshita Gupta, Salman Khan, Guolei Sun, Fahad Shahbaz Khan, Fan Zhu, Ling Shao, Gui-Song Xia, Xiang Bai
Aerial Image Segmentation Dataset	80 high-resolution aerial images with spatial resolution ranging from 0.3 to 1.0.	Images manually segmented.	80	Images	Aerial Classification, object detection	2013	^[133] ^[134]	J. Yuan et al.
KIT AIS Data Set	Multiple labeled training and evaluation datasets of aerial images of crowds.	Images manually labeled to show paths of individuals through crowds.	~ 150	Images with paths	People tracking, aerial tracking	2012	^[135] ^[136]	M. Butenuth et al.
Wilt Dataset	Remote sensing data of diseased trees and other land cover.	Various features extracted.	4899	Images	Classification, aerial object detection	2014	^[137] ^[138]	B. Johnson
MASATI dataset	Maritime scenes of optical aerial images from the visible spectrum. It contains color images in dynamic marine environments, each image may contain one or multiple targets in different weather and illumination conditions.	Object bounding boxes and labeling.	7389	Images	Classification, aerial object detection	2018	^[139] ^[140]	A.-J. Gallego et al.
Forest Type Mapping Dataset	Satellite imagery of forests in Japan.	Image wavelength bands extracted.	326	Text	Classification	2015	^[141] ^[142]	B. Johnson
Overhead Imagery Research Data Set	Annotated overhead imagery. Images with multiple objects.	Over 30 annotations and over 60 statistics that describe the target within the context of the image.	1000	Images, text	Classification	2009	^[143] ^[144]	F. Tanner et al.
SpaceNet	SpaceNet is a corpus of commercial satellite imagery and labeled training data.	GeoTiff and GeoJSON files containing building footprints.	>17533	Images	Classification, Object Identification	2017	^[145] ^[146] ^[147]	DigitalGlobe, Inc.
UC Merced Land Use Dataset	These images were manually extracted from large images from the USGS National Map Urban Area Imagery collection for various urban areas around the US.	This is a 21 class land use image dataset meant for research purposes. There are 100 images for each class.	2,100	Image chips of 256x256, 30 cm (1 foot) GSD	Land cover classification	2010	^[148]	Yi Yang and Shawn Newsam
SAT-4 Airborne Dataset	Images were extracted from the National Agriculture Imagery Program (NAIP) dataset.	SAT-4 has four broad land cover classes, includes barren land, trees, grassland and a class that consists of all land cover classes other than the above three.	500,000	Images	Classification	2015	^[149] ^[150]	S. Basu et al.
SAT-6 Airborne Dataset	Images were extracted from the National Agriculture Imagery Program (NAIP) dataset.	SAT-6 has six broad land cover classes, includes barren land, trees, grassland, roads, buildings and water bodies.	405,000	Images	Classification	2015		S. Basu et al.

Underwater images

Dataset name	Brief description	Preprocessing	Instances	Format	Default Task	Created (updated)	Reference	Creator
SUIM Dataset	The images have been rigorously collected during oceanic explorations and human-robot collaborative experiments, and annotated by human participants.	Images with pixel annotations for eight object categories: fish (vertebrates), reefs (invertebrates), aquatic plants, wrecks/ruins, human divers, robots, and sea-floor.	1,635	Images	Segmentation	2020	^[151]	Md Jahidul Islam et al.
LIACI Dataset	Images have been collected during underwater ship inspections and annotated by human domain experts.	Images with pixel annotations for ten object categories: defects, corrosion, paint peel, marine growth, sea chest gratings, overboard valves, propeller, anodes, bilge keel and ship hull.	1,893	Images	Segmentation	2022	^[152]	Waszak et al.

Other images

Dataset name	Brief description	Preprocessing	Instances	Format	Default Task	Created (updated)	Reference	Creator
NRC-GAMMA	A novel benchmark gas meter image dataset	None	28,883	Image, Label	Classification	2021	^[153] ^[154]	A. Ebadi, P. Paul, S. Auer, & S. Tremblay
The SUPATLANTIQUE dataset	Images of scanned official and Wikipedia documents	None	4908	TIFF/pdf	Source device identification, forgery detection, Classification,..	2020	^[155]	C. Ben Rabah et al.
Density functional theory quantum simulations of graphene	Labelled images of raw input to a simulation of graphene	Raw data (in HDF5 format) and output labels from density functional theory quantum simulation	60744 test and 501473 training files	Labeled images	Regression	2019		K. Mills & I. Tamblyn
Quantum simulations of an electron in a two dimensional potential well	Labelled images of raw input to a simulation of 2d Quantum mechanics	Raw data (in HDF5 format) and output labels from quantum simulation	1.3 million images	Labeled images	Regression	2017	^[156]	K. Mills, M.A. Spanner, & I. Tamblyn
MPII Cooking Activities Dataset	Videos and images of various cooking activities.	Activity paths and directions, labels, fine-grained motion labeling, activity class, still image extraction and labeling.	881,755 frames	Labeled video, images, text	Classification	2012	^[157] ^[158]	M. Rohrbach et al.
FAMOS Dataset	5,000 unique microstructures, all samples have been acquired 3 times with two different cameras.	Original PNG files, sorted per camera and then per acquisition. MATLAB datafiles with one 16384 times 5000 matrix per camera per acquisition.	30,000	Images and .mat files	Authentication	2012	^[159]	S. Voloshynovskiy, et al.
PharmaPack Dataset	1,000 unique classes with 54 images per class.	Class labeling, many local descriptors, like SIFT and aKaZE, and local feature agreators, like Fisher Vector (FV).	54,000	Images and .mat files	Fine-grain classification	2017	^[160]	O. Taran and S. Rezaeifar, et al.
Stanford Dogs Dataset	Images of 120 breeds of dogs from around the world.	Train/test splits and ImageNet annotations provided.	20,580	Images, text	Fine-grain classification	2011	^[161] ^[162]	A. Khosla et al.
StanfordExtra Dataset	2D keypoints and segmentations for the Stanford Dogs Dataset.	2D keypoints and segmentations provided.	12,035	Labelled images	3D reconstruction/pose estimation	2020	^[163]	B. Biggs et al.
The Oxford-IIIT Pet Dataset	37 categories of pets with roughly 200 images of each.	Breed labeled, tight bounding box, foreground-background segmentation.	~ 7,400	Images, text	Classification, object detection	2012	^[164]	O. Parkhi et al.
Corel Image Features Data Set	Database of images with features extracted.	Many features including color histogram, co-occurrence texture, and colormoments,	68,040	Text	Classification, object detection	1999	^[165] ^[166]	M. Ortega-Bindenberger et al.
Online Video Characteristics and Transcoding Time Dataset.	Transcoding times for various different videos and video properties.	Video features given.	168,286	Text	Regression	2015	^[167]	T. Deneke et al.
Microsoft Sequential Image Narrative Dataset (SIND)	Dataset for sequential vision-to-language	Descriptive caption and storytelling given for each photo, and photos are arranged in sequences	81,743	Images, text	Visual storytelling	2016	^[168]	Microsoft Research
Caltech-UCSD Birds-200-2011 Dataset	Large dataset of images of birds.	Part locations for birds, bounding boxes, 312 binary attributes given	11,788	Images, text	Classification	2011	^[169] ^[170]	C. Wah et al.
YouTube-8M	Large and diverse labeled video dataset	YouTube video IDs and associated labels from a diverse vocabulary of 4800 visual entities	8 million	Video, text	Video classification	2016	^[171] ^[172]	S. Abu-El-Haija et al.
YFCC100M	Large and diverse labeled image and video dataset	Flickr Videos and Images and associated description, titles, tags, and other metadata (such as EXIF and geotags)	100 million	Video, Image, Text	Video and Image classification	2016	^[173] ^[174]	B. Thomee et al.
Discrete LIRIS-ACCEDE	Short videos annotated for valence and arousal.	Valence and arousal labels.	9800	Video	Video emotion elicitation detection	2015	^[175]	Y. Baveye et al.
Continuous LIRIS-ACCEDE	Long videos annotated for valence and arousal while also collecting Galvanic Skin Response.	Valence and arousal labels.	30	Video	Video emotion elicitation detection	2015	^[176]	Y. Baveye et al.
MediaEval LIRIS-ACCEDE	Extension of Discrete LIRIS-ACCEDE including annotations for violence levels of the films.	Violence, valence and arousal labels.	10900	Video	Video emotion elicitation detection	2015	^[177]	Y. Baveye et al.
Leeds Sports Pose	Articulated human pose annotations in 2000 natural sports images from Flickr.	Rough crop around single person of interest with 14 joint labels	2000	Images plus .mat file labels	Human pose estimation	2010	^[178]	S. Johnson and M. Everingham
Leeds Sports Pose Extended Training	Articulated human pose annotations in 10,000 natural sports images from Flickr.	14 joint labels via crowdsourcing	10000	Images plus .mat file labels	Human pose estimation	2011	^[179]	S. Johnson and M. Everingham
MCQ Dataset	6 different real multiple choice-based exams (735 answer sheets and 33,540 answer boxes) to evaluate computer vision techniques and systems developed for multiple choice test assessment systems.	None	735 answer sheets and 33,540 answer boxes	Images and .mat file labels	Development of multiple choice test assessment systems	2017	^[180] ^[181]	Afifi, M. et al.
Surveillance Videos	Real surveillance videos cover a large surveillance time (7 days with 24 hours each).	None	19 surveillance videos (7 days with 24 hours each).	Videos	Data compression	2016	^[182]	Taj-Eddin, I. A. T. F. et al.
LILA BC	Labeled Information Library of Alexandria: Biology and Conservation. Labeled images that support machine learning research around ecology and environmental science.	None	~10M images	Images	Classification	2019	^[183]	LILA working group
Can We See Photosynthesis?	32 videos for eight live and eight dead leaves recorded under both DC and AC lighting conditions.	None	32 videos	Videos	Liveness detection of plants	2017	^[184]	Taj-Eddin, I. A. T. F. et al.
Mathematical Mathematics Memes	Collection of 10,000 memes on mathematics.	None	~10,000	Images	Visual storytelling, object detection.	2021	^[185]	Mathematical Mathematics Memes
Flickr-Faces-HQ Dataset	Collection of images containing a face each, crawled from Flickr	Pruned with "various automatic filters", cropped and aligned to faces, and had images of statues, paintings, or photos of photos removed via crowdsourcing	70,000	Images	Face Generation	2019	^[186]	Karras et al.
Fruits-360 dataset	Database with images of 131 fruits, vegetables and nuts.	100x100 pixels, white background.	90483	Images (jpg)	Classification	2017–2024	^[187]	Mihai Oltean

References

1. Grauman . Kristen . Westbury . Andrew . Byrne . Eugene . Chavis . Zachary . Furnari . Antonino . Girdhar . Rohit . Hamburger . Jackson . Jiang . Hao . Liu . Miao . Liu . Xingyu . Martin . Miguel . Nagarajan . Tushar . Radosavovic . Ilija . Ramakrishnan . Santhosh Kumar . Ryan . Fiona . Sharma . Jayant . Wray . Michael . Xu . Mengmeng . Xu . Eric Zhongcong . Zhao . Chen . Bansal . Siddhant . Batra . Dhruv . Cartillier . Vincent . Crane . Sean . Do . Tien . Doulaty . Morrie . Erapalli . Akshay . Feichtenhofer . Christoph . Fragomeni . Adriano . Fu . Qichen . Gebreselasie . Abrham . Gonzalez . Cristina . Hillis . James . Huang . Xuhua . Huang . Yifei . Jia . Wenqi . Khoo . Weslie . Kolar . Jachym . Kottur . Satwik . Kumar . Anurag . Landini . Federico . Li . Chao . Li . Yanghao . Li . Zhenqiang . Mangalam . Karttikeya . Modhugu . Raghava . Munro . Jonathan . Murrell . Tullie . Nishiyasu . Takumi . Price . Will . Puentes . Paola Ruiz . Ramazanova . Merey . Sari . Leda . Somasundaram . Kiran . Southerland . Audrey . Sugano . Yusuke . Tao . Ruijie . Vo . Minh . Wang . Yuchen . Wu . Xindi . Yagi . Takuma . Zhao . Ziwei . Zhu . Yunyi . Arbelaez . Pablo . Crandall . David . Damen . Dima . Farinella . Giovanni Maria . Fuegen . Christian . Ghanem . Bernard . Ithapu . Vamsi Krishna . Jawahar . C. V. . Joo . Hanbyul . Kitani . Kris . Li . Haizhou . Newcombe . Richard . Oliva . Aude . Park . Hyun Soo . Rehg . James M. . Sato . Yoichi . Shi . Jianbo . Shou . Mike Zheng . Torralba . Antonio . Torresani . Lorenzo . Yan . Mingfei . Malik . Jitendra . Ego4D: Around the World in 3,000 Hours of Egocentric Video . 2022 . cs.CV . 2110.07058.
2. 10.1007/s11263-016-0981-7. Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations. International Journal of Computer Vision. 123. 32–73. 2017. Krishna. Ranjay. Zhu. Yuke. Groth. Oliver. Johnson. Justin. Hata. Kenji. Kravitz. Joshua. Chen. Stephanie. Kalantidis. Yannis. Li. Li-Jia. Shamma. David A. Bernstein. Michael S. Fei-Fei. Li. 1602.07332. 4492210.
3. Karayev, S., et al. "A category-level 3-D object dataset: putting the Kinect to work." Proceedings of the IEEE International Conference on Computer Vision Workshops. 2011.
4. Tighe, Joseph, and Svetlana Lazebnik. "Superparsing: scalable nonparametric image parsing with superpixels ." Computer Vision–ECCV 2010. Springer Berlin Heidelberg, 2010. 352–365.
5. Arbelaez. P.. Maire. M. Fowlkes. C. Malik. J. Contour Detection and Hierarchical Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence . May 2011. 33. 5. 898–916. 27 February 2016. 10.1109/tpami.2010.161. 20733228. 206764694.
6. 1405.0312 . Lin . Tsung-Yi . Maire . Michael . Belongie . Serge . Bourdev . Lubomir . Girshick . Ross . Hays . James . Perona . Pietro . Ramanan . Deva . Lawrence Zitnick . C. . Dollár . Piotr . Microsoft COCO: Common Objects in Context . 2014 . cs.CV .
7. Russakovsky . Olga . et al . 2015 . Imagenet large scale visual recognition challenge . International Journal of Computer Vision . 115 . 3. 211–252 . 10.1007/s11263-015-0816-y. 1409.0575 . 1721.1/104944 . 2930547 .
8. Web site: COCO – Common Objects in Context. cocodataset.org.
9. Xiao, Jianxiong, et al. "Sun database: Large-scale scene recognition from abbey to zoo." Computer vision and pattern recognition (CVPR), 2010 IEEE conference on. IEEE, 2010.
10. 1310.1531. Donahue. Jeff. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition . Jia. Yangqing. Vinyals. Oriol. Hoffman. Judy. Zhang. Ning. Tzeng. Eric. Darrell. Trevor. cs.CV. 2013.
11. Deng, Jia, et al. "Imagenet: A large-scale hierarchical image database."Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009.
12. Russakovsky. Olga. Deng. Jia. Su. Hao. Krause. Jonathan. Satheesh. Sanjeev. Ma. Sean. Huang. Zhiheng. Karpathy. Andrej. Khosla. Aditya. Bernstein. Michael. Berg. Alexander C.. Fei-Fei. Li. 5. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision. 11 April 2015. 115. 3. 211–252. 10.1007/s11263-015-0816-y. 1409.0575. 1721.1/104944. 2930547.
13. Ivan Krasin, Tom Duerig, Neil Alldrin, Andreas Veit, Sami Abu-El-Haija, Serge Belongie, David Cai, Zheyun Feng, Vittorio Ferrari, Victor Gomes, Abhinav Gupta, Dhyanesh Narayanan, Chen Sun, Gal Chechik, Kevin Murphy. "OpenImages: A public dataset for large-scale multi-label and multi-class image classification, 2017. Available from https://github.com/openimages."
14. Vyas, Apoorv, et al. "Commercial Block Detection in Broadcast News Videos." Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing. ACM, 2014.
15. Hauptmann, Alexander G., and Michael J. Witbrock. "Story segmentation and detection of commercials in broadcast news video." Research and Technology Advances in Digital Libraries, 1998. ADL 98. Proceedings. IEEE International Forum on. IEEE, 1998.
16. Tung, Anthony KH, Xin Xu, and Beng Chin Ooi. "Curler: finding and visualizing nonlinear correlation clusters." Proceedings of the 2005 ACM SIGMOD international conference on Management of data. ACM, 2005.
17. Jarrett, Kevin, et al. "What is the best multi-stage architecture for object recognition?." Computer Vision, 2009 IEEE 12th International Conference on. IEEE, 2009.
18. [Svetlana Lazebnik|Lazebnik, Svetlana]
19. Griffin, G., A. Holub, and P. Perona. Caltech-256 object category dataset California Inst. Technol., Tech. Rep. 7694, 2007. Available: http://authors.library.caltech.edu/7694, 2007.
20. Baeza-Yates, Ricardo, and Berthier Ribeiro-Neto. Modern information retrieval. Vol. 463. New York: ACM press, 1999.
21. Fu, Xiping, et al. "NOKMeans: Non-Orthogonal K-means Hashing." Computer Vision—ACCV 2014. Springer International Publishing, 2014. 162–177.
22. Heitz . Geremy . et al . 2009 . Shape-based object localization for descriptive classification . International Journal of Computer Vision . 84 . 1. 40–62 . 10.1007/s11263-009-0228-y. 10.1.1.142.280 . 646320 .
23. Everingham . Mark . et al . 2010 . The pascal visual object classes (voc) challenge . International Journal of Computer Vision . 88 . 2. 303–338 . 10.1007/s11263-009-0275-4. 20.500.11820/88a29de3-6220-442b-ab2d-284210cf72d6 . 4246903 . free .
24. Felzenszwalb . Pedro F. . et al . 2010 . Object detection with discriminatively trained part-based models . IEEE Transactions on Pattern Analysis and Machine Intelligence . 32 . 9. 1627–1645 . 10.1109/tpami.2009.167. 20634557 . 10.1.1.153.2745 . 3198903 .
25. Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.
26. Gong, Yunchao, and Svetlana Lazebnik. "Iterative quantization: A procrustean approach to learning binary codes." Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 2011.
27. Web site: CINIC-10 dataset. Luke N. Darlow, Elliot J. Crowley, Antreas Antoniou, Amos J. Storkey (2018) CINIC-10 is not ImageNet or CIFAR-10. 2018-11-13. 2018-10-09.
28. Web site: notMNIST dataset. Machine Learning, etc. 2017-10-13. 2011-09-08.
29. Chaladze, G., Kalatozishvili, L. (2017). Linnaeus 5 dataset. Chaladze.com. Retrieved 13 November 2017, from http://chaladze.com/l5/
30. Afifi. Mahmoud. 2017-11-12. Gender recognition and biometric identification using a large dataset of hand images. 1711.04322. cs.CV.
31. Lomonaco. Vincenzo. Maltoni. Davide. 2017-10-18. CORe50: a New Dataset and Benchmark for Continuous Object Recognition. 1705.03550. cs.CV.
32. She. Qi. Feng. Fan. Hao. Xinyue. Yang. Qihan. Lan. Chuanlin. Lomonaco. Vincenzo. Shi. Xuesong. Wang. Zhengwei. Guo. Yao. Zhang. Yimin. Qiao. Fei. Chan. Rosa H.M.. 2019-11-15. OpenLORIS-Object: A Robotic Vision Dataset and Benchmark for Lifelong Deep Learning. 1911.06487v2. cs.CV.
33. Web site: THz and thermal video data set. Morozov. Alexei. Sushkova. Olga. 2019-06-13. IRE RAS. Development of the multi-agent logic programming approach to a human behaviour analysis in a multi-channel video surveillance. 2019-07-19. Moscow.
34. Morozov. Alexei. Sushkova. Olga. Kershner. Ivan. Polupanov. Alexander. 2019-07-09. Development of a method of terahertz intelligent video surveillance based on the semantic fusion of terahertz and 3D video images. CEUR. 2391. paper19. 2019-07-19.
35. M. Cordts, M. Omran, S. Ramos, T. Scharwächter, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, "The Cityscapes Dataset." In CVPR Workshop on The Future of Datasets in Vision, 2015.
36. Houben, Sebastian, et al. "Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark." Neural Networks (IJCNN), The 2013 International Joint Conference on. IEEE, 2013.
37. Mathias, Mayeul, et al. "Traffic sign recognition—How far are we from the solution?." Neural Networks (IJCNN), The 2013 International Joint Conference on. IEEE, 2013.
38. Geiger, Andreas, Philip Lenz, and Raquel Urtasun. "Are we ready for autonomous driving? the kitti vision benchmark suite." Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012.
39. Sturm, Jürgen, et al. "A benchmark for the evaluation of RGB-D SLAM systems." Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on. IEEE, 2012.
40. Kragh . Mikkel F. . et al . 2017 . FieldSAFE – Dataset for Obstacle Detection in Agriculture . Sensors . 17 . 11 . 2579. 10.3390/s17112579 . 29120383 . 5713196 . 2017Senso..17.2579K. 1709.03526 . free .
41. Web site: Papers with Code - Daimler Monocular Pedestrian Detection Dataset . paperswithcode.com . 5 May 2023 . en.
42. Enzweiler . Markus . Gavrila . Dariu M. . Monocular Pedestrian Detection: Survey and Experiments . IEEE Transactions on Pattern Analysis and Machine Intelligence . December 2009 . 31 . 12 . 2179–2195 . 10.1109/TPAMI.2008.260 . 19834140 . 1192198 . 1939-3539.
43. Yin . Guojun . Liu . Bin . Zhu . Huihui . Gong . Tao . Yu . Nenghai . A Large Scale Urban Surveillance Video Dataset for Multiple-Object Tracking and Behavior Analysis . 28 July 2020 . cs.CV . 1904.11784 .
44. Web site: Object Recognition in Video Dataset . mi.eng.cam.ac.uk . 5 May 2023.
45. Book: Brostow . Gabriel J. . Shotton . Jamie . Fauqueur . Julien . Cipolla . Roberto . Computer Vision – ECCV 2008 . Segmentation and Recognition Using Structure from Motion Point Clouds . Lecture Notes in Computer Science . 2008 . 5302 . 44–57 . 10.1007/978-3-540-88682-2_5 . https://link.springer.com/chapter/10.1007/978-3-540-88682-2_5 . Springer . 978-3-540-88681-5 . en.
46. Brostow . Gabriel J. . Fauqueur . Julien . Cipolla . Roberto . Semantic object classes in video: A high-definition ground truth database . Pattern Recognition Letters . 15 January 2009 . 30 . 2 . 88–97 . 10.1016/j.patrec.2008.04.005 . 2009PaReL..30...88B . en . 0167-8655.
47. Web site: WildDash 2 Benchmark . wilddash.cc . 5 May 2023.
48. Book: Zendel . Oliver . Murschitz . Markus . Zeilinger . Marcel . Steininger . Daniel . Abbasi . Sara . Beleznai . Csaba . 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . RailSem19: A Dataset for Semantic Rail Scene Understanding . June 2019 . 1221–1229 . 10.1109/CVPRW.2019.00161 . 978-1-7281-2506-0 . 198166233 . https://ieeexplore.ieee.org/document/9025646.
49. Web site: The Boreas Dataset . www.boreas.utias.utoronto.ca . 5 May 2023.
50. Burnett . Keenan . Yoon . David J. . Wu . Yuchen . Li . Andrew Zou . Zhang . Haowei . Lu . Shichen . Qian . Jingxing . Tseng . Wei-Kang . Lambert . Andrew . Leung . Keith Y. K. . Schoellig . Angela P.. Angela Schoellig . Barfoot . Timothy D. . Boreas: A Multi-Season Autonomous Driving Dataset . 26 January 2023 . cs.RO . 2203.10168 .
51. Web site: Bosch Small Traffic Lights Dataset . hci.iwr.uni-heidelberg.de . 5 May 2023 . en . 1 March 2017.
52. Book: Behrendt . Karsten . Novak . Libor . Botros . Rami . 2017 IEEE International Conference on Robotics and Automation (ICRA) . A deep learning approach to traffic lights: Detection, tracking, and classification . May 2017 . 1370–1377 . 10.1109/ICRA.2017.7989163 . 978-1-5090-4633-1 . 6257133 . https://ieeexplore.ieee.org/document/7989163.
53. Web site: FRSign Dataset . frsign.irt-systemx.fr . 5 May 2023.
54. Harb . Jeanine . Rébéna . Nicolas . Chosidow . Raphaël . Roblin . Grégoire . Potarusov . Roman . Hajri . Hatem . FRSign: A Large-Scale Traffic Light Dataset for Autonomous Trains . 5 February 2020 . cs.CY . 2002.05665 .
55. Web site: ifs-rwth-aachen/GERALD . Chair and Institute for Rail Vehicles and Transport Systems . 5 May 2023 . 30 April 2023.
56. Leibner . Philipp . Hampel . Fabian . Schindler . Christian . GERALD: A novel dataset for the detection of German mainline railway signals . Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit . 3 April 2023 . 237 . 10 . 1332–1342 . 10.1177/09544097231166472 . 257939937 . en . 0954-4097.
57. Book: Wojek . Christian . Walk . Stefan . Schiele . Bernt . 2009 IEEE Conference on Computer Vision and Pattern Recognition . Multi-cue onboard pedestrian detection . June 2009 . 794–801 . 10.1109/CVPR.2009.5206638 . 978-1-4244-3992-8 . 18000078 . https://ieeexplore.ieee.org/document/5206638.
58. Toprak . Tuğçe . Aydın . Burak . Belenlioğlu . Burak . Güzeliş . Cüneyt . Selver . M. Alper . Conditional Weighted Ensemble of Transferred Models for Camera Based Onboard Pedestrian Detection in Railway Driver Support Systems. IEEE Transactions on Vehicular Technology . 5 May 2023 . 5 April 2020. 1 . 10.1109/TVT.2020.2983825 . 216510283 .
59. Toprak . Tugce . Belenlioglu . Burak . Aydın . Burak . Guzelis . Cuneyt . Selver . M. Alper . Conditional Weighted Ensemble of Transferred Models for Camera Based Onboard Pedestrian Detection in Railway Driver Support Systems . IEEE Transactions on Vehicular Technology . May 2020 . 69 . 5 . 5041–5054 . 10.1109/TVT.2020.2983825 . 216510283 . 1939-9359.
60. Tilly . Roman . Neumaier . Philipp . Schwalbe . Karsten . Klasek . Pavel . Tagiew . Rustam . Denzler . Patrick . Klockau . Tobias . Boekhoff . Martin . Köppel . Martin . Open Sensor Data for Rail 2023 . 2023 . 10.57806/9mv146r0 . de.
61. Book: Tagiew . Rustam . Köppel . Martin . Schwalbe . Karsten . Denzler . Patrick . Neumaier . Philipp . Klockau . Tobias . Boekhoff . Martin . Klasek . Pavel . Tilly . Roman . 2023 8th International Conference on Robotics and Automation Engineering (ICRAE) . OSDaR23: Open Sensor Data for Rail 2023 . 4 May 2023 . 270–276 . 10.1109/ICRAE59816.2023.10458449 . 2305.03001 . 979-8-3503-2765-6 .
62. Web site: Home . Argoverse . 5 May 2023.
63. Chang . Ming-Fang . Lambert . John . Sangkloy . Patsorn . Singh . Jagjeet . Bak . Slawomir . Hartnett . Andrew . Wang . De . Carr . Peter . Lucey . Simon . Ramanan . Deva . Hays . James . Argoverse: 3D Tracking and Forecasting with Rich Maps . 6 November 2019 . cs.CV . 1911.02620 .
64. Book: Zafeiriou. S.. Kollias. D.. Nicolaou. M.A.. Papaioannou. A.. Zhao. G.. Kotsia. I.. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . Aff-Wild: Valence and Arousal 'In-the-Wild' Challenge . 2017. https://eprints.mdx.ac.uk/22045/1/aff_wild_kotsia.pdf. 1980–1987. 10.1109/CVPRW.2017.248. 978-1-5386-0733-6. 3107614.
65. Kollias. D.. Tzirakis. P.. Nicolaou. M.A.. Papaioannou. A.. Zhao. G.. Schuller. B.. Kotsia. I.. Zafeiriou. S.. 2019. Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond. International Journal of Computer Vision . 127. 6–7. 907–929. 10.1007/s11263-019-01158-4. 13679040. free. 1804.10938.
66. Kollias. D.. Zafeiriou. S.. 2019. Expression, affect, action unit recognition: Aff-wild2, multi-task learning and arcface. British Machine Vision Conference (BMVC), 2019. 1910.04855.
67. Book: Kollias. D.. Schulc. A.. Hajiyev. E.. Zafeiriou. S.. 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) . Analysing Affective Behavior in the First ABAW 2020 Competition . 2020. https://www.computer.org/csdl/proceedings-article/fg/2020/307900a794/1kecIYu9wL6. 637–643. 10.1109/FG47880.2020.00126. 2001.11409. 978-1-7281-3079-8. 210966051.
68. Phillips . P. Jonathon . et al . 1998 . The FERET database and evaluation procedure for face-recognition algorithms . Image and Vision Computing . 16 . 5. 295–306 . 10.1016/s0262-8856(97)00070-x.
69. Wiskott . Laurenz . et al . 1997 . Face recognition by elastic bunch graph matching . IEEE Transactions on Pattern Analysis and Machine Intelligence . 19 . 7. 775–779 . 10.1109/34.598235. 10.1.1.44.2321 . 30523165 .
70. 10.1371/journal.pone.0196391. 29768426. 5955500. The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLOS ONE. 13. 5. e0196391. 2018. Livingstone. Steven R.. Russo. Frank A.. 2018PLoSO..1396391L. free.
71. Book: 10.5281/zenodo.1188976. 2018. Livingstone. Steven R.. The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). Russo. Frank A.. Emotion.
72. Grgic . Mislav . Delac . Kresimir . Grgic . Sonja . 2011 . SCface–surveillance cameras face database . Multimedia Tools and Applications . 51 . 3. 863–879 . 10.1007/s11042-009-0417-2 . 207218990 .
73. Wallace, Roy, et al. "Inter-session variability modelling and joint factor analysis for face authentication." Biometrics (IJCB), 2011 International Joint Conference on. IEEE, 2011.
74. Georghiades . A . Yale face database . Center for Computational Vision and Control at Yale University. 2 . 1997 .
75. Nguyen . Duy . et al . 2006 . Real-time face detection and lip feature extraction using field-programmable gate arrays . IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics. 36 . 4. 902–912 . 10.1109/tsmcb.2005.862728. 16903373 . 10.1.1.156.9848 . 7334355 .
76. [Takeo Kanade|Kanade, Takeo]
77. Zeng . Zhihong . et al . 2009 . A survey of affect recognition methods: Audio, visual, and spontaneous expressions . IEEE Transactions on Pattern Analysis and Machine Intelligence . 31 . 1. 39–58 . 10.1109/tpami.2008.52. 19029545 . 10.1.1.144.217 .
78. Book: 10.5281/zenodo.3451524. 1998. Lyons. Michael. The Japanese Female Facial Expression (JAFFE) Database. Kamachi. Miyuki. Gyoba. Jiro. Facial expression images.
79. Lyons, Michael; Akamatsu, Shigeru; Kamachi, Miyuki; Gyoba, Jiro "Coding facial expressions with Gabor wavelets." Automatic Face and Gesture Recognition, 1998. Proceedings. Third IEEE International Conference on. IEEE, 1998.
80. Ng, Hong-Wei, and Stefan Winkler. "A data-driven approach to cleaning large face datasets ." Image Processing (ICIP), 2014 IEEE International Conference on. IEEE, 2014.
81. 1506.01342. RoyChowdhury. Aruni. One-to-many face recognition with bilinear CNNs. Lin. Tsung-Yu. Maji. Subhransu. Learned-Miller. Erik. cs.CV. 2015.
82. Jesorsky, Oliver, Klaus J. Kirchberg, and Robert W. Frischholz. "Robust face detection using the hausdorff distance." Audio-and video-based biometric person authentication. Springer Berlin Heidelberg, 2001.
83. Huang, Gary B., et al. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Vol. 1. No. 2. Technical Report 07-49, University of Massachusetts, Amherst, 2007.
84. Bhatt, Rajen B., et al. "Efficient skin region segmentation using low complexity fuzzy decision tree model." India Conference (INDICON), 2009 Annual IEEE. IEEE, 2009.
85. Lingala . Mounika . et al . 2014 . Fuzzy logic color detection: Blue areas in melanoma dermoscopy images . Computerized Medical Imaging and Graphics . 38 . 5. 403–410 . 10.1016/j.compmedimag.2014.03.007. 24786720 . 4287461 .
86. Maes, Chris, et al. "Feature detection on 3D face surfaces for pose normalisation and recognition." Biometrics: Theory Applications and Systems (BTAS), 2010 Fourth IEEE International Conference on. IEEE, 2010.
87. Savran, Arman, et al. "Bosphorus database for 3D face analysis." Biometrics and Identity Management. Springer Berlin Heidelberg, 2008. 47–56.
88. Heseltine, Thomas, Nick Pears, and Jim Austin. "Three-dimensional face recognition: An eigensurface approach." Image Processing, 2004. ICIP'04. 2004 International Conference on. Vol. 2. IEEE, 2004.
89. Ge . Yun . et al . 2011 . 3D Novel Face Sample Modeling for Face Recognition . Journal of Multimedia . 6 . 5. 467–475 . 10.4304/jmm.6.5.467-475. 10.1.1.461.9710 .
90. Wang . Yueming . Liu . Jianzhuang . Tang . Xiaoou . 2010 . Robust 3D face recognition by local shape difference boosting . IEEE Transactions on Pattern Analysis and Machine Intelligence . 32 . 10. 1858–1870 . 10.1109/tpami.2009.200. 20724762 . 10.1.1.471.2424 . 15263913 .
91. Zhong, Cheng, Zhenan Sun, and Tieniu Tan. "Robust 3D face recognition using learned visual codebook." Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on. IEEE, 2007.
92. Zhao . G. . Huang . X. . Taini . M. . Li . S. Z. . Pietikäinen . M. . 2011 . Facial expression recognition from near-infrared videos . Image and Vision Computing . 29 . 9. 607–619 . 10.1016/j.imavis.2011.07.002 .
93. Soyel, Hamit, and Hasan Demirel. "Facial expression recognition using 3D facial feature distances." Image Analysis and Recognition. Springer Berlin Heidelberg, 2007. 831–838.
94. Bowyer . Kevin W. . Chang . Kyong . Flynn . Patrick . 2006 . A survey of approaches and challenges in 3D and multi-modal 3D+ 2D face recognition . Computer Vision and Image Understanding . 101 . 1. 1–15 . 10.1016/j.cviu.2005.05.005. 10.1.1.134.8784 .
95. Tan . Xiaoyang . Triggs . Bill . 2010 . Enhanced local texture feature sets for face recognition under difficult lighting conditions . IEEE Transactions on Image Processing. 19 . 6. 1635–1650 . 10.1109/tip.2010.2042645. 20172829 . 2010ITIP...19.1635T . 10.1.1.105.3355 . 4943234 .
96. Book: https://ieeexplore.ieee.org/document/4529822 . 10.1109/ICIS.2008.77 . Three Dimensional Face Recognition Using SVM Classifier . Seventh IEEE/ACIS International Conference on Computer and Information Science (Icis 2008) . 2008 . Mousavi . Mir Hashem . Faez . Karim . Asghari . Amin . 208–213 . 978-0-7695-3131-1 . 2710422 .
97. Book: 2008 . 978-1-4244-2154-1 . 10.1109/AFGR.2008.4813376 . https://gravis.dmi.unibas.ch/publications/2008/FG08_Amberg.pdf . dead . Expression invariant 3D face recognition with a Morphable Model . 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition . Amberg . Brian . Knothe . Reinhard . Vetter . Thomas . 1–6 . 5651453 . 6 August 2019 . 28 July 2018 . https://web.archive.org/web/20180728233944/http://gravis.dmi.unibas.ch/publications/2008/FG08_Amberg.pdf .
98. Book: https://www.researchgate.net/publication/4090704 . 10.1109/ICPR.2004.1333734. 3D shape-based face recognition using automatically registered facial surfaces. Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. 2004. Irfanoglu. M.O.. Gokberk. B.. Akarun. L.. 183–186 Vol.4. 0-7695-2128-2. 10987293.
99. Beumier . Charles . Acheroy . Marc . 2001 . Face verification from 3D and grey level clues . Pattern Recognition Letters . 22 . 12. 1321–1329 . 10.1016/s0167-8655(01)00077-0. 2001PaReL..22.1321B .
100. Afifi. Mahmoud. Abdelhamed. Abdelrahman. 2017-06-13. AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces. 1706.04277. cs.CV.
101. Web site: SoF dataset. sites.google.com. en-US. 2017-11-18.
102. Web site: IMDb-WIKI. data.vision.ee.ethz.ch. en-US. 2018-03-13.
103. Patron-Perez . A. . Marszalek . M. . Reid . I. . Zisserman . A. . 2012 . Structured learning of human interactions in TV shows . IEEE Transactions on Pattern Analysis and Machine Intelligence . 34 . 12. 2441–2453 . 10.1109/tpami.2012.24. 23079467 . 6060568 .
104. Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., & Bajcsy, R. (January 2013). Berkeley MHAD: A comprehensive multimodal human action database. In Applications of Computer Vision (WACV), 2013 IEEE Workshop on (pp. 53–60). IEEE.
105. Jiang, Y. G., et al. "THUMOS challenge: Action recognition with a large number of classes." ICCV Workshop on Action Recognition with a Large Number of Classes, http://crcv.ucf.edu/ICCV13-Action-Workshop. 2013.
106. Simonyan, Karen, and Andrew Zisserman. "Two-stream convolutional networks for action recognition in videos." Advances in Neural Information Processing Systems. 2014.
107. 10.1109/TCSVT.2015.2475835. Fast Action Localization in Large-Scale Video Archives. IEEE Transactions on Circuits and Systems for Video Technology. 26. 10. 1917–1930. 2016. Stoian. Andrei. Ferecatu. Marin. Benois-Pineau. Jenny. Crucianu. Michel. 31537462.
108. Botta, M., A. Giordana, and L. Saitta. "Learning fuzzy concept definitions." Fuzzy Systems, 1993., Second IEEE International Conference on. IEEE, 1993.
109. Frey . Peter W. . Slate . David J. . 1991 . Letter recognition using Holland-style adaptive classifiers . Machine Learning . 6 . 2. 161–182 . 10.1007/bf00114162. free .
110. Peltonen . Jaakko . Klami . Arto . Kaski . Samuel . 2004 . Improved learning of Riemannian metrics for exploratory analysis . Neural Networks . 17 . 8. 1087–1100 . 10.1016/j.neunet.2004.06.008. 15555853 . 10.1.1.59.4865 .
111. Online and offline handwritten Chinese character recognition: Benchmarking on new databases . Pattern Recognition . 46 . 1 . January 2013 . 155–162 . Cheng-Lin . Liu . Fei . Yin . Da-Han . Wang . Qiu-Feng . Wang . 10.1016/j.patcog.2012.06.021 . 2013PatRe..46..155L .
112. Book: Wang . D. . C. . Liu . J. . Yu . X. . Zhou . 2009 10th International Conference on Document Analysis and Recognition . CASIA-OLHWDB1: A Database of Online Handwritten Chinese Characters . 2009 . 1206–1210. 10.1109/ICDAR.2009.163 . 978-1-4244-4500-4 . 5705532 .
113. Williams, Ben H., Marc Toussaint, and Amos J. Storkey. Extracting motion primitives from natural handwriting data. Springer Berlin Heidelberg, 2006.
114. Meier, Franziska, et al. "Movement segmentation using a primitive library."Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on. IEEE, 2011.
115. T. E. de Campos, B. R. Babu and M. Varma. Character recognition in natural images. In Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP), Lisbon, Portugal, February 2009
116. 1702.05373v1. Cohen. Gregory. Afshar. Saeed. Tapson. Jonathan. André van Schaik. EMNIST: An extension of MNIST to handwritten letters. 2017. cs.CV .
117. The EMNIST Dataset. NIST . 4 April 2017.
118. 1702.05373 . Cohen . Gregory . Afshar . Saeed . Tapson . Jonathan . André van Schaik . EMNIST: An extension of MNIST to handwritten letters . 2017 . cs.CV .
119. Llorens, David, et al. "The UJIpenchars Database: a Pen-Based Database of Isolated Handwritten Characters." LREC. 2008.
120. Calderara . Simone . Prati . Andrea . Cucchiara . Rita . 2011 . Mixtures of von mises distributions for people trajectory shape analysis . IEEE Transactions on Circuits and Systems for Video Technology. 21 . 4. 457–471 . 10.1109/tcsvt.2011.2125550. 1427766 .
121. Guyon, Isabelle, et al. "Result analysis of the nips 2003 feature selection challenge." Advances in neural information processing systems. 2004.
122. Lake. B. M.. Salakhutdinov. R.. Tenenbaum. J. B.. 2015-12-11. Human-level concept learning through probabilistic program induction. Science. en. 350. 6266. 1332–1338. 10.1126/science.aab3050. 0036-8075. 26659050. 2015Sci...350.1332L. free.
123. LeCun . Yann . et al . 1998 . Gradient-based learning applied to document recognition . Proceedings of the IEEE . 86 . 11. 2278–2324 . 10.1109/5.726791. 10.1.1.32.9552 . 14542261 .
124. Kussul . Ernst . Baidyk . Tatiana . Tetyana Baydyk. 2004 . Improved method of handwritten digit recognition tested on MNIST database . Image and Vision Computing . 22 . 12. 971–981 . 10.1016/j.imavis.2004.03.008 .
125. Xu . Lei . Krzyżak . Adam . Suen . Ching Y. . 1992 . Methods of combining multiple classifiers and their applications to handwriting recognition . IEEE Transactions on Systems, Man, and Cybernetics. 22 . 3. 418–435 . 10.1109/21.155943. 10338.dmlcz/135217 .
126. Alimoglu, Fevzi, et al. "Combining multiple classifiers for pen-based handwritten digit recognition." (1996).
127. Tang . E. Ke . et al . 2005 . Linear dimensionality reduction using relevance weighted LDA . Pattern Recognition . 38 . 4. 485–493 . 10.1016/j.patcog.2004.09.005. 2005PatRe..38..485T . 10580110 .
128. Hong, Yi, et al. "Learning a mixture of sparse distance metrics for classification and dimensionality reduction." Computer Vision (ICCV), 2011 IEEE International Conference on. IEEE, 2011.
129. 1701.08380. Thoma. Martin. The HASYv2 dataset. cs.CV. 2017.
130. Karki. Manohar. Liu. Qun. DiBiano. Robert. Basu. Saikat. Mukhopadhyay. Supratik. 2018-06-20. Pixel-level Reconstruction and Classification for Noisy Handwritten Bangla Characters. 1806.08037. cs.CV.
131. Web site: iSAID. 2021-11-30. captain-whu.github.io.
132. Zamir, Syed & Arora, Aditya & Gupta, Akshita & Khan, Salman & Sun, Guolei & Khan, Fahad & Zhu, Fan & Shao, Ling & Xia, Gui-Song & Bai, Xiang. (2019). iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images. website
133. Yuan . Jiangye . Gleason . Shaun S. . Cheriyadat . Anil M. . 2013 . Systematic benchmarking of aerial image segmentation . IEEE Geoscience and Remote Sensing Letters. 10 . 6. 1527–1531 . 10.1109/lgrs.2013.2261453. 2013IGRSL..10.1527Y . 629629 .
134. Vatsavai, Ranga Raju. "Object based image classification: state of the art and computational challenges." Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data. ACM, 2013.
135. Butenuth, Matthias, et al. "Integrating pedestrian simulation, tracking and event detection for crowd analysis." Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on. IEEE, 2011.
136. Fradi, Hajer, and Jean-Luc Dugelay. "Low level crowd analysis using frame-wise normalized feature for people counting." Information Forensics and Security (WIFS), 2012 IEEE International Workshop on. IEEE, 2012.
137. Johnson, Brian Alan, Ryutaro Tateishi, and Nguyen Thanh Hoan. "A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees." International journal of remote sensing34.20 (2013): 6969–6982.
138. A new classification model for a class imbalanced data set using genetic programming and support vector machines: Case study for wilt disease classification. 10.1080/2150704X.2015.1062159. 2015. Mohd Pozi. Muhammad Syafiq. Sulaiman. Md Nasir. Mustapha. Norwati. Perumal. Thinagaran. Remote Sensing Letters. 6. 7. 568–577. 2015RSL.....6..568M . 58788630.
139. Gallego, A.-J.; Pertusa, A.; Gil, P. "Automatic Ship Classification from Optical Aerial Images with Convolutional Neural Networks." Remote Sensing. 2018; 10(4):511.
140. Gallego, A.-J.; Pertusa, A.; Gil, P. "MAritime SATellite Imagery dataset". Available: https://www.iuii.ua.es/datasets/masati/, 2018.
141. Johnson . Brian . Tateishi . Ryutaro . Xie . Zhixiao . 2012 . Using geographically weighted variables for image classification . Remote Sensing Letters . 3 . 6. 491–499 . 10.1080/01431161.2011.629637. 2012RSL.....3..491J . 122543681 .
142. Chatterjee, Sankhadeep, et al. "Forest Type Classification: A Hybrid NN-GA Model Based Approach." Information Systems Design and Intelligent Applications. Springer India, 2016. 227–236.
143. Diegert, Carl. "A combinatorial method for tracing objects using semantics of their shape." Applied Imagery Pattern Recognition Workshop (AIPR), 2010 IEEE 39th. IEEE, 2010.
144. Razakarivony, Sebastien, and Frédéric Jurie. "Small target detection combining foreground and background manifolds." IAPR International Conference on Machine Vision Applications. 2013.
145. Web site: SpaceNet. explore.digitalglobe.com. 2018-03-13. 13 March 2018. https://web.archive.org/web/20180313092809/http://explore.digitalglobe.com/spacenet. dead.
146. Web site: Getting Started With SpaceNet Data. Etten. Adam Van. 2017-01-05. The DownLinQ. 2018-03-13.
147. Book: Vakalopoulou. M.. Bus. N.. Karantzalosa. K.. Paragios. N.. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) . Integrating edge/Boundary priors with classification scores for building detection in very high resolution data . July 2017. 3309–3312. 10.1109/IGARSS.2017.8127705. 978-1-5090-4951-6. 8297433.
148. Book: Yang. Yi. Newsam. Shawn. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems . Bag-of-visual-words and spatial extensions for land-use classification . 2010. 270–279 . New York, New York, USA. ACM Press. 10.1145/1869790.1869829. 9781450304283. 993769.
149. Book: Basu. Saikat. Ganguly. Sangram. Mukhopadhyay. Supratik. DiBiano. Robert. Karki. Manohar. Nemani. Ramakrishna. Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems . DeepSat: A learning framework for satellite imagery . 2015-11-03. ACM. 1–10. 10.1145/2820783.2820816. 9781450339674. 4387134.
150. Liu. Qun. Basu. Saikat. Ganguly. Sangram. Mukhopadhyay. Supratik. DiBiano. Robert. Karki. Manohar. Nemani. Ramakrishna. 2019-11-21. DeepSat V2: feature augmented convolutional neural nets for satellite image classification. Remote Sensing Letters. 11. 2. 156–165. 10.1080/2150704x.2019.1693071. 1911.07747. 208138097. 2150-704X.
151. Md Jahidul Islam, et al. "Semantic Segmentation of Underwater Imagery: Dataset and Benchmark." 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020.
152. Waszak et al. "Semantic Segmentation in Underwater Ship Inspections: Benchmark and Data Set." IEEE Journal of Oceanic Engineering. IEEE, 2022.
153. Ebadi. Ashkan. Paul. Patrick. Auer. Sofia. Tremblay. Stéphane. 2021-11-12. NRC-GAMMA: Introducing a Novel Large Gas Meter Image Dataset. cs.CV. 2111.06827.
154. Canada. Government of Canada National Research Council. The gas meter image dataset (NRC-GAMMA) - NRC Digital Repository. 2021-12-02. nrc-digital-repository.canada.ca. 2021. 10.4224/3c8s-z290.
155. Book: Rabah. Chaima Ben. Coatrieux. Gouenou. Abdelfattah. Riadh. 2020 IEEE International Conference on Image Processing (ICIP) . The Supatlantique Scanned Documents Database for Digital Image Forensics Purposes . October 2020. http://dx.doi.org/10.1109/icip40778.2020.9190665. 2096–2100. IEEE. 10.1109/icip40778.2020.9190665. 978-1-7281-6395-6. 224881147.
156. Book: 10.4224/PhysRevA.96.042113.data. Quantum simulations of an electron in a two dimensional potential well. 2018-05-16. Mills. Kyle. Spanner. Michael. Tamblyn. Isaac. Quantum simulation. National Research Council of Canada.
157. Rohrbach . M. . Amin . S. . Andriluka . M. . Schiele . B. . 2012 IEEE Conference on Computer Vision and Pattern Recognition . A database for fine grained activity detection of cooking activities . IEEE . 2012 . 1194–1201 . 978-1-4673-1228-8 . 10.1109/cvpr.2012.6247801 .
158. Kuehne, Hilde, Ali Arslan, and Thomas Serre. "The language of actions: Recovering the syntax and semantics of goal-directed human activities."Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014.
159. Sviatoslav, Voloshynovskiy, et al. "Towards Reproducible results in authentication based on physical non-cloneable functions: The Forensic Authentication Microstructure Optical Set (FAMOS)."Proc. Proceedings of IEEE International Workshop on Information Forensics and Security. 2012.
160. Olga, Taran and Shideh, Rezaeifar, et al. "PharmaPack: mobile fine-grained recognition of pharma packages."Proc. European Signal Processing Conference (EUSIPCO). 2017.
161. Khosla, Aditya, et al. "Novel dataset for fine-grained image categorization: Stanford dogs."Proc. CVPR Workshop on Fine-Grained Visual Categorization (FGVC). 2011.
162. Parkhi, Omkar M., et al. "Cats and dogs."Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012.
163. Book: 2007.11110 . 10.1007/978-3-030-58621-8 . Computer Vision – ECCV 2020 . Lecture Notes in Computer Science . 2020 . 12356 . 978-3-030-58620-1 . Biggs . Benjamin . Boyne . Oliver . Charles . James . Fitzgibbon . Andrew . Cipolla . Roberto . 227173931 .
164. Razavian, Ali, et al. "CNN features off-the-shelf: an astounding baseline for recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2014.
165. Ortega . Michael . et al . 1998 . Supporting ranked boolean similarity queries in MARS . IEEE Transactions on Knowledge and Data Engineering. 10 . 6. 905–925 . 10.1109/69.738357. 10.1.1.36.6079 .
166. He, Xuming, Richard S. Zemel, and Miguel Á. Carreira-Perpiñán. "[ftp://www-vhost.cs.toronto.edu/public_html/public_html/dist/zemel/Papers/cvpr04.pdf Multiscale conditional random fields for image labeling]." Computer vision and pattern recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE computer society conference on. Vol. 2. IEEE, 2004.
167. Deneke, Tewodros, et al. "Video transcoding time prediction for proactive load balancing." Multimedia and Expo (ICME), 2014 IEEE International Conference on. IEEE, 2014.
168. Ting-Hao (Kenneth) Huang, Francis Ferraro, Nasrin Mostafazadeh, Ishan Misra, Aishwarya Agrawal, Jacob Devlin, Ross Girshick, Xiaodong He, Pushmeet Kohli, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh, Lucy Vanderwende, Michel Galley, Margaret Mitchell . 1604.03968 . Visual Storytelling . cs.CL . 13 April 2016 .
169. Wah, Catherine, et al. "The caltech-ucsd birds-200-2011 dataset." (2011).
170. Duan, Kun, et al. "Discovering localized attributes for fine-grained recognition." Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012.
171. Web site: YouTube-8M Dataset. research.google.com. 1 October 2016.
172. Abu-El-Haija, Sami . Kothari, Nisarg . Lee, Joonseok . Natsev, Paul . Toderici, George . Varadarajan, Balakrishnan . Vijayanarasimhan, Sudheendra . 1609.08675 . YouTube-8M: A Large-Scale Video Classification Benchmark . cs.CV . 27 September 2016 .
173. Web site: YFCC100M Dataset. mmcommons.org. Yahoo-ICSI-LLNL. 1 June 2017.
174. Bart Thomee . David A Shamma . Gerald Friedland . Benjamin Elizalde . Karl Ni . Douglas Poland . Damian Borth . Li-Jia Li . 1503.01817 . Yfcc100m: The new data in multimedia research . 25 April 2016 . 10.1145/2812802 . 59 . 2 . Communications of the ACM . 64–73 . 207230134 .
175. Y. Baveye, E. Dellandrea, C. Chamaret, and L. Chen, "LIRIS-ACCEDE: A Video Database for Affective Content Analysis," in IEEE Transactions on Affective Computing, 2015.
176. Y. Baveye, E. Dellandrea, C. Chamaret, and L. Chen, "Deep Learning vs. Kernel Methods: Performance for Emotion Prediction in Videos," in 2015 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), 2015.
177. M. Sjöberg, Y. Baveye, H. Wang, V. L. Quang, B. Ionescu, E. Dellandréa, M. Schedl, C.-H. Demarty, and L. Chen, "The mediaeval 2015 affective impact of movies task," in MediaEval 2015 Workshop, 2015.
178. S. Johnson and M. Everingham, "Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation ", in Proceedings of the 21st British Machine Vision Conference (BMVC2010)
179. S. Johnson and M. Everingham, "Learning Effective Human Pose Estimation from Inaccurate Annotation ", In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR2011)
180. Afifi. Mahmoud. Hussain. Khaled F.. 2017-11-02. The Achievement of Higher Flexibility in Multiple Choice-based Tests Using Image Classification Techniques. 1711.00972. cs.CV.
181. Web site: MCQ Dataset. sites.google.com. en-US. 2017-11-18.
182. Book: Taj-Eddin. I. A. T. F.. Afifi. M.. Korashy. M.. Hamdy. D.. Nasser. M.. Derbaz. S.. 2016 Sixth International Conference on Digital Information and Communication Technology and its Applications (DICTAP) . A new compression technique for surveillance videos: Evaluation using new dataset . July 2016. 159–164. 10.1109/DICTAP.2016.7544020. 978-1-4673-9609-7. 8698850.
183. Tabak. Michael A.. Norouzzadeh. Mohammad S.. Wolfson. David W.. Sweeney. Steven J.. Vercauteren. Kurt C.. Snow. Nathan P.. Halseth. Joseph M.. Di Salvo. Paul A.. Lewis. Jesse S.. White. Michael D.. Teton. Ben. Beasley. James C.. Schlichting. Peter E.. Boughton. Raoul K.. Wight. Bethany. Newkirk. Eric S.. Ivan. Jacob S.. Odell. Eric A.. Brook. Ryan K.. Lukacs. Paul M.. Moeller. Anna K.. Mandeville. Elizabeth G.. Clune. Jeff. Miller. Ryan S.. Photopoulou. Theoni. Machine learning to classify animal species in camera trap images: Applications in ecology. Methods in Ecology and Evolution. 10. 4. 585–590. 2018. 2041-210X. 10.1111/2041-210X.13120. free.
184. Taj-Eddin. Islam A. T. F.. Afifi. Mahmoud. Korashy. Mostafa. Ahmed. Ali H.. Ng. Yoke Cheng. Hernandez. Evelyng. Abdel-Latif. Salma M.. November 2017. Can we see photosynthesis? Magnifying the tiny color changes of plant green leaves using Eulerian video magnification. Journal of Electronic Imaging. 26. 6. 060501. 10.1117/1.jei.26.6.060501. 1017-9909. 1706.03867. 2017JEI....26f0501T. 12367169.
185. Web site: Mathematical Mathematics Memes .
186. Book: Karras . Tero . Laine . Samuli . Aila . Timo . 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . A Style-Based Generator Architecture for Generative Adversarial Networks . June 2019 . http://dx.doi.org/10.1109/cvpr.2019.00453 . 4396–4405 . IEEE . 10.1109/cvpr.2019.00453. 1812.04948 . 978-1-7281-3293-8 . 54482423 .
187. Web site: Oltean. Mihai . 2017 . Fruits-360 dataset. .