Concept-based image indexing explained

Concept-based image indexing, also variably named as "description-based" or "text-based" image indexing/retrieval, refers to retrieval from text-based indexing of images that may employ keywords, subject headings, captions, or natural language text (Chen & Rasmussen, 1999). It is opposed to Content-based image retrieval. Indexing is a technique used in CBIR.

Chu (2001) confirms that there exist two distinctive research groups employing the content-based and description-based approaches, respectively. However, research in the content-based domain is currently dominating in the field, while the other approach has less visibility.

See also

• Document classification
• Subject (documents)

References

• Ahmad, K., M. Tariq, B. Vrusias and C.Handy. 2003. Corpus-based thesaurus construction for image retrieval in specialist domains. In Sebastiani, F. (ed.). Proceedings of the 25th European Conference on Information Retrieval Research (ECIR-03). 502–510. Heidelberg: Springer Verlag.
  • Angeles, M. (1998). Information Organization and Information Use of Visual Resources Collections. VRA Bulletin, 25 (3), 51-58. https://archive.today/20130205065426/http://urlgreyhot.com/personal/publications/information_organization_and_information_use_of_visual_resources?PHPSESSID=05f07e15bb719a05b4c621657f8cd897
  • Chen, H.-L., & Rasmussen, E.M. (1999). Intellectual access to images. Library Trends, 48(2), 291–302.
  • Chu, H. T. (2001). Research in image indexing and retrieval as reflected in the literature. Journal of the American Society for Information Science and Technology, 52(12), 1011-1018.
  • Fidel, R.; Hahn, T. B.; Rasmussen, E. M. & Smith, P. J. (1994). Challenges in Indexing Electronic Text and Images. Medford, NJ: Learned Information. (ASIS Monograph Series)
  • Heidorn, P. B. & Sandore, B. (Eds.). (1997). Digital Image Access & Retrieval: Proceedings of the 1996 Clinic on Library Applications of Data Processing. Illinois: University of Illinois, Graduate School of Library and Information Science.
  • Jörgensen, C. (2003). Image Retrieval. Theory and Research. Lanham, Maryland: Scarecrow Press.
  • Landbeck, C. R. (2002). The organization and categorization of political cartoons: An exploratory study. The Florida State University, School of Information Studies. (Master of Science thesis). https://web.archive.org/web/20120331122537/http://etd.lib.fsu.edu/theses/available/etd-06272003-144515/unrestricted/crl01.pdf
  • Lamy-Rousseau, F. (1984). Classification des images, materiels et donnees = Classification of images, materials and data . 2nd ed. Longueuil, Quebec: F. Lamy-Rousseau.
  • Panofsky, E. (1962). Studies in Icology: Humanistic themes in the art of the Renaissance. New York: Harper & Row.
  • Rasmussen, E. M. (1997). Indexing images. Annual Review of Information Science and Technology, 32, 169-196.
  • Shatford, S. (1986). Analyzing the Subject of a Picture: A Theoretical Approach. Cataloging and Classification Quarterly, 6(3), 39-62.
  • Wang, J. Z. (2001). Integrated Region-Based Image Retrieval. Boston, MA: Kluwer Academic Publishers. Book review: http://www-db.stanford.edu/~wangz/project/kluwer/1/review.pdf
  • Wang, Xin; Erdelez, Sanda; Allen, Carla; Anderson, Blake; Cao, Hongfei & Shyu, Chi-Ren (2011). Role of Domain Knowledge in Developing User-Centered Medical-Image Indexing. Journal of the American Society for Information Science and Technology, early view October 2011.
  • Warden, G.; Dunbar, D.; Wanczycki, C. & O'Hanley, S. (2002). The Subject Analysis of Images: Past, Present and Future. https://web.archive.org/web/20080726185732/http://www.slais.ubc.ca/PEOPLE/students/student-projects/C_Wanczycki/libr517/homepage.html
  • Ørnager, S. (1997). Image retrieval - Theoretical analysis and empirical user studies on accessing information in images. Proceedings of the ASIS annual meeting, 34, 202-211.