Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31100
Text Retrieval Relevance Feedback Techniques for Bag of Words Model in CBIR

Authors: Nhu Van NGUYEN, Jean-Marc OGIER, Salvatore TABBONE, Alain BOUCHER


The state-of-the-art Bag of Words model in Content- Based Image Retrieval has been used for years but the relevance feedback strategies for this model are not fully investigated. Inspired from text retrieval, the Bag of Words model has the ability to use the wealth of knowledge and practices available in text retrieval. We study and experiment the relevance feedback model in text retrieval for adapting it to image retrieval. The experiments show that the techniques from text retrieval give good results for image retrieval and that further improvements is possible.

Keywords: Image Retrieval, relevance feedback, probabilistic model, vector space model, bag of words model

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1744


[1] G. Salton, A. Wong, and C. S. Yang, "A Vector Space Model for Automatic Indexing," Communications of the ACM, vol. 18, 1975, nr. 11, pp. 613-620.
[2] D. G. Lowe. Distinctive image features from scale-invariant key points. International Journal of Computer Vision, 60(2), 2004, pp. 91-110.
[3] K. Barnard, P. Duygulu, N. de Freitas, F. Forsyth, D. Blei, and M. Jordan. "Matching words and pictures," Journal of Machine Learning Research 3, 2003, pp. 1107-1135
[4] Y. Rui, T. S. Huang, M. Ortega, and S. Mehrotra. "Relevance feedback: A power tool in interactive content-based image retrieval," IEEE Transactions on Circuits and Systems for Video Technology, 8(5), 1998, pp. 644-655.
[5] L. Fei-Fei and P. Perona, "A Bayesian Hierarchical Model for Learning Natural Scene Categories," Proc. of IEEE Computer Vision and Pattern Recognition, 2005, pp. 524-531
[6] M. Vidal-Naquet and S. Ullman, "Object recognition with informative features and linear classification," Proc. of IEEE International Conference on Computer Vision, 2003, pp. 281-288
[7] J. Urban, J. Jose, and K. van Rijsbergen, "An adaptive approach towards content based image retrieval," In Proceedings of the International Workshop Content-Based Multimedia Indexing, 2003, Rennes, France.
[8] J. Vogel and B. Schiele, "On Performance Characterization and Optimization for Image Retrieval," Proc. of European Conference on Computer Vision, 2002, pp. 51-55
[9] A. Boucher, H. Dang, L. Le, "Classification vs recherche d-information : vers une caractérisation des bases d-images," 12èmes Rencontres de la Société Francophone de Classification (SFC), 2005, Montréal, Canada.
[10] M. Crucianu, M. Ferecatu and N. Boujemaa, "Relevance feedback for image retrieval: a short review, " In State of the Art in Audiovisual Content-Based Retrieval, Information Universal Access and Interaction including Data models and Languages (DELOS2 Report, 2004.
[11] A. Singhal. "Modern information retrieval: A brief overview," Bulletin of the IEEE Computer Society Technical Committeeon Data Engineering, 24(4), 2001, pp. 35-42
[12] A. Singhal, J. Choi, D. Hindle, D. Lewis, and F. Pereira. "AT&T at TREC-7," In Proc. of the Seventh Text REtrieval Conference (TREC-7), NIST Special Publication, 1999, pp. 239-252.
[13] J.J. Rocchio, "Relevance feedback in information retrieval". In The SMART Retrieval System," Experiments in Automatic Document Processing, Prentice Hall Inc, 1971, pp. 313-323, Englewood Cliffs, NJ.
[14] Y. Rui, T. Huang and S. Mehrotra, "Content-based image retrieval with relevance eedback in Mars," In Proceedings of the IEEE International Conference on Image Processing, 1997, Washington, DC.
[15] H. Fang, C. Zhai, "An exploration of axiomatic approaches to information retrieval," In Proceedings of the 28th ACM Conference on Research and Development in Information Retrieval, 2005, pp. 480- 487.
[16] N. Fuhr, "Probabilistic models in information retrieval," The Computer Journal, 35(3), 1992, pp 243-255.
[17] D. Heesch, S. Ruger, "Interaction models and relevance feedback in image retrieval," Semantic-Based VisualInformation Retrieval, 1st ed, IRM Press, pp. 160-186.
[18] Y. Rui and T. Huang, "Optimizing learning in image retrieval," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2000, Hilton Head Island, SC.
[19] L. Le, A. Boucher, M. Thonnat, F. Bremond, "A framework for surveillance video indexing and retrieval," Content-Based Multimedia Indexing, CBMI 2008, 2008 pp. 338 - 345.
[20] H. M├╝ller, W. M├╝ller, D. Squire, S. Marchand-Maillet and T. Pun, "Strategies forpositive and negative relevance feedback in image retrieval," In Proceedings of theInternational Conference on Pattern Recognition, 2000, Barcelona, Spain.
[21] J. Sivic and A. Zisserman, "Efficient Visual Search for Objects in Videos," In Proceedings of the IEEE, Special Issue on Advances in Multimedia Information Retrieval, 96(4), 2008, pp. 548-566.
[22] J. Yang, C-W. Ngo, A. Hauptmann and Y-G. Jiang, "Evaluating Bag-of- Visual-Words Representations in Scene Classification," ACM Multimedia Information Retrieval Workshop at ACM Multimedia 2007, 2007, Augsburg, Germany.
[23] P. Tirilly, V. Claveau and P. Gros, "Language modeling for bag-ofvisual words image categorization," In CIVR '08: Proceedings of the2008 international conference on Content-based image and video retrieval, 2008, pp. 249-258, New York, NY, USA.
[24] S. Karthik, C-V. Jawahar, "Discriminative Relevance Feedback With Virtual Textual Representation for Efficient Image Retrieval," Visual Information Engineering VIE 2006. IET International Conference, 2006, pp. 309 - 31.
[25] K. Spärck Jones, S. Walker, and S. E. Robertson, "A Probabilistic Model of Information Retrieval: Development and Comparative Experiments," Information Processing and Management, 36(6), 2000, pp.779-840.
[26] J. Yang, Y. G. Jiang, A. G. Hauptmann, C. W. Ngo, "Evaluating Bagof- Visual-Words Representations in Scene Classification," ACM SIGMM Int'l Workshop on Multimedia Information Retrieval (MIR'07), 2007, Augsburg, Germany,