Relevance feedback for CBIR: A new approach based on probabilistic feature weighting with positive and negative examples

IEEE Transactions on Image Processing

Volumn 15, Issue 4, 2006, Pages 1017-1030

  Kherfi, Mohammed Lamine ^a   Ziou, Djemel ^b  

^a UNIVERSITÉ DU QUÉBEC À TROIS RIVIÈRES   (Canada)

^b UNIVERSITÉ DE SHERBROOKE   (Canada)

Author keywords

Content based image retrieval (CBIR); Feature selection (FS); Relevance feedback (RF)

Indexed keywords

ALGORITHMS; FEEDBACK; MATHEMATICAL MODELS; PROBABILITY;

CONTENT-BASED IMAGE RETRIEVAL (CBIR); FEATURE SELECTION (FS); IMAGE FEATURES; PROBABILISTIC FEATURE; RELEVANCE FEEDBACK (RF);

IMAGE RETRIEVAL;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER INTERFACE; COMPUTER SIMULATION; DATA BASE; EVALUATION; FACTUAL DATABASE; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; METHODOLOGY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DATABASE MANAGEMENT SYSTEMS; DATABASES, FACTUAL; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; USER-COMPUTER INTERFACE;

EID: 33645135960     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2005.863969     Document Type: Article

References (43)

1. M. Flickner, H. Sawhney, W. Niblack, J. Sahley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker, "Query by image and video content: The QBIC system," IEEE Computer, vol. 28, no. 9, pp. 23-32, Sep. 1995.
2. V. E. Ogle and M. Stonebraker, "Chabot: Retrieval from a relational database of images," IEEE Computer, vol. 28, no. 9, pp. 40-48, Sep. 1995.
3. J. Z. Wang, J. Li, and G. Wiederhold, "SIMPLIcity: Semantics-sensitive integrated matching for picture libraries," IEEE Trans. Pattern Anal. Machine Intell., vol. 23, no. 9, pp. 947-963, Sep. 2001.
4. Y. Rui, T. S. Huang, and S. Mehrotra, "Content-based image retrieval with relevance feedback in MARS," in Proc. IEEE Int. Conf. Image Processing, Santa Barbara, CA, 1997, pp. 815-818.
5. I. J. Cox, M. L. Miller, S. M. Omohundro, and P. N. Yianilos, "PicHunter: Bayesian relevance feedback for image retrieval," in Proc. Int. Conf. Pattern Recognition, Vienna, Austria, 1996, pp. 361-369.
6. J. Bach, C. Fuller, A. Gupta, A. Hampapur, B. Gorowitz, R. Humphrey, R. Jain, and C. Shu, "Virage image search engine: An open framework for image management," in Proc. SPIE Conf. Storage and Retrieval for Image and Video Databases IV, San Jose, CA, 1996.
7. M. L. Kherfi, D. Ziou, and A. Bernardi, "Combining positive and negative examples in relevance feedback for content-based image retrieval," J. Vis. Commun. Image Represent., vol. 14, no. 4, pp. 428-457, 2003.
8. X. S. Zhou, N. Petrovic, and T. S. Huang, "Comparing discriminating transformations and SVM for learning during multimedia retrieval," in ACM Multimedia, Ottawa, ON, Canada, 2001, pp. 137-146.
9. J. Li and J. Z. Wang, "Automatic linguistic indexing of pictures by a statistical modeling approach," IEEE Trans. Pattern Anal. Machine Intell., vol. 25, no. 9, pp. 1075-1088, Sep. 2003.
10. K. Barnard and D. Forsyth, "Learning the semantics of words and pictures," presented at the Int. Conf. Computer Vision, Vancouver, BC, Canada, 2001.
11. Y. Lu, C. Hu, X. Zhu, H. Zhang, and Q. Yang, "A unified framework for semantics and feature based relevance feedback in image retrieval systems," presented at the ACM Int. Conf. Multimedia, Marina del Rey, CA, 2000.
12. A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, "Content-based image retrieval at the end of the early years," IEEE Trans. Pattern Anal. Machine Intell., vol. 22, no. 12, pp. 1349-1380, Dec. 2000.
13. M. L. Kherfi, D. Ziou, and A. Bernardi, "Image retrieval from the world wide web: Issnums, techniques, and systems," ACM Comput. Surv., vol. 36, no. 1, 2004.
14. Y. Rui, T. S. Huang, and S. F. Chang, "Image retrieval: Current techniques, promising directions and open issnums," J. Vis. Commun. Image Represent., vol. 10, no. 4, pp. 39-62, 1999.
15. J. J. Rocchio Jr., "Relevance feedback in information retrieval," in The Smart System - Experiments in Automatic Document Processing. Englewood Cliffs, NJ: Prentice-Hall, 1971, pp. 313-323.
16. E. Ide, "New experiments in relevance feedback," in The Smart System-Experiments in Automatic Document Processing. Englewood Cliffs, NJ: Prentice-Hall, 1971, pp. 337-354.
17. G. Salton, "Relevance feedback and the optimization of retrieval effectiveness," in The Smart System - Experiments in Automatic Document Processing. Englewood Cliffs, NJ: Prentice-Hall, 1971, pp. 324-336.
18. N. Vasconcelos and A. Lippman, "Embedded mixture modeling for efficient probabilistic content-based indexing and retrieval," presented at the SPIE Conf. Multimedia Storage and Archiving Systems III, Boston, MA, 1998.
19. P. Hong, Q. Tian, and T. S. Huang, "Incorporate support vector machines to content-based image retrieval with relevance feedback," presented at the Int. Conf. Image Processing, Vancouver, BC, Canada, 2000.
20. Y. Chen, X. S. Zhou, and T. S. Huang, "One-class SVM for learning in image retrieval," presented at the Int. Conf. Image Processing, Thessaloniki, Greece, 2001.
21. Y. Ishikawa, R. Subramanya, and C. Faloutsos, "Mindreader: Querying databases through multiple examples," in Proc. 24th Int. Conf. Very Large Data Bases, New York, 1998, pp. 433-438.
22. H. Müller, W. Müller, D. M. Squire, S. Marchand-Maillet, and T. Pun, "Strategies for Positive and Negative Relevance Feedback in Image Retrieval," Comput. Vis. Group, Comput. Center, Univ. Geneva, Geneva, Switzerland, Tech. Rep., 2000.
23. N. Vasconcelos and A. Lippman, "Learning from user feedback in image retrieval systems," Neur. Inf. Process. Syst., 1999.
24. C. Meilhac and C. Nastar, "Relevance feedback and category search in image databases," in Proc. IEEE Int. Conf. Multimedia Computing and Systems, Florence, Italy, 1999, pp. 512-517.
25. Z. Su, H.-J. Zhang, S. Li, and S. Ma, "Relevance feedback in content-based image retrieval: Bayesian framework, feature sub-spaces, and progressive learning," IEEE Trans. Image Process., vol. 12, no. 8, pp. 924-937, Aug. 2003.
26. D. Tao and X. Tang, "Random sampling based SVM for relevance feedback image retrieval," presented at the Int. Conf. Computer Vision and Pattern Recognition, Washington, DC, 2004.
27. S. Tong and E. Chang, "Support vector machine active learning for image retrieval," presented at the ACM Multimedia Conf., Ottawa, ON, Canada, 2001.
28. J. Laaksonen, M. Koskela, and E. Oja, "PicSOM: Self-organizing maps for content-based image retrieval," presented at the Int. Joint Conf. Neural Networks, Washington, DC, 1999.
29. I. J. Cox, T. P. Minka, T. V. Papathomas, and P. N. Yianilos, "The Bayesian image retrieval system, PicHunter: Theory, implementation, and psychophysical experiments," IEEE Trans. Image Process., vol. 9, no. 1, pp. 20-37, Jan. 2000.
30. S. D. MacArthur, C. E. Brodley, and C.-R. Shyu, "Relevance feedback decision trees in content-based image retrieval," presented at the IEEE Workshop on Content-based Access of Image and Video Libraries, Hilton Head, SC, 2000.
31. Y. Rui and T. S. Huang, "Optimizing learning in image retrieval," presented at the IEEE Int. Conf. Computer Vision and Pattern Recognition, Hilton Head, SC, 2000.
32. D. Tao and X. Tang, "Nonparametric discriminant analysis in relevance feedback for content-based image retrieval," presented at the Int. Conf. Pattern Recognition, Cambridge, U.K., 2004.
33. K.-K. Sung and T. Poggio, "Example-Based Learning for View-Based Human Face Detection," Mass. Inst. Technol., Cambridge, Tech. Rep., Dec. 1994.
34. M. Nakazato, C. Dagli, and T. S. Huang, "Evaluating group-based relevance feedback for content-based image retrieval," presented at the IEEE Int. Conf. Image Processing, Barcelona, Spain, 2003.
35. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. Roy. Statist. Soc. B, vol. 39, no. 1, pp. 1-38, 1977.
36. K. Fukunaga, Statistical Pattern Recognition, 2nd ed. San Diego, CA: Academic, 1990.
37. H. Akaike, "A new look at the statistical model identification," IEEE Trans. Autom. Control, vol. 19, no. 6, pp. 716-723, Dec. 1974.
38. M. S. Allili, D. Ziou, and L. Bentabet, "A robust level set approach for image segmentation and statistical modeling," presented at the Advanced Concepts for Intelligent Vision Systems, Brussels, Belgium, 2004.
39. G. Paschos, "Perceptually uniform color spaces for color texture analysis: An empirical evaluation," IEEE Trans. Image Process., vol. 10, no. 6, pp. 932-937, Jun. 2001.
40. R. Haralick, K. Shanmugan, and I. Dinstein, "Textural features for image classification," IEEE Tran. Syst., Man, Cybern., vol. SMC-6, no. 3, pp. 610-621, Jun. 1973.
41. T. Randen and J. H. Husoy, "Filtering for texture classification: A comparative study," IEEE Trans. Pattern Anal. Machine Intell., vol. 21, no. 4, pp. 291-310, Apr. 1999.
42. M. Unser, "Sum and difference histograms for texture classification," IEEE Trans. Pattern Anal. Machine Intell., vol. 8, no. 1, pp. 118-125, Jan. 1996.
43. http://elib.cs.berkeley.edu/photos/index.shtml