A unified relevance feedback framework for web image retrieval

IEEE Transactions on Image Processing

Volumn 18, Issue 6, 2009, Pages 1350-1357

  Cheng, En ^a   Jing, Feng ^b   Zhang, Lei ^c  

^a Case Western Reserve University   (United States)

^b TENCENT AI LAB   (China)

^c MICROSOFT RESEARCH ASIA   (China)

Author keywords

Implicit feedback; Relevance feedback (RF); Search result clustering; Web image retrieval

Indexed keywords

CLICKTHROUGH DATA; CONTENT BASED IMAGE RETRIEVAL; FEATURE-BASED; FEEDBACK PROCESS; FUSION STRATEGIES; IMPLICIT FEEDBACK; QUERY CONCEPTS; RELEVANCE FEEDBACK; RELEVANCE FEEDBACK (RF); SEARCH RESULT CLUSTERING; SEARCH RESULTS; VISUAL FEATURE; WEB IMAGE RETRIEVAL; WEB IMAGE SEARCH; WEB IMAGES;

CLUSTERING ALGORITHMS; CONTENT BASED RETRIEVAL; INFORMATION RETRIEVAL; SEARCH ENGINES; USER INTERFACES; WORLD WIDE WEB;

FEEDBACK;

EID: 67349237757     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2009.2017128     Document Type: Article

References (19)

1. Google Image Search, [Online]. Available: http://images.google.com
2. Yahoo Image Search, [Online]. Available: http:// images.search.yahoo.com/
3. Altavisa Image Search, [Online]. Available: http:// www.altavista.com/image/
4. Picsearch Image Search, [Online]. Available: http:// www.picsearch.com
5. J. Rocchio, Relevance Feedback in Information Retrieval. Upper Saddle River, NJ: Prentice-Hall, 1971.
6. X. S. Zhou and T. S. Huang, "Relevance feedback in image retrieval: A comprehensive review," ACM Multimedia Syst., vol. 8, no. 6, pp. 536-544, 2003.
7. Q. K. Zhao, S. C. H. Hoi, T. Y. Liu, S. S. Bhowmick, M. R. Lyu, and W. Y. Ma, "Time-dependent semantic similarity measure of queries using historical click-through data," in Proc. 15th Int. Conf. World Wide Web, 2006, pp. 543-552.
8. T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay, "Accurately interpreting clickthrough data as implicit feedback," in Proc. 28th Annu. Int. ACM SIGIR Conf. Research and Development in Information Retrieval, 2005, pp. 154-161.
9. Photosig, [Online]. Available: http://www.photosig.com
10. R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval. Reading, MA: Addison-Wesley, 1999.
11. L. Zhang, Y. X. Hu, M. J. Li, W. Y. Ma, and H. J. Zhang, "Efficient propagation for face annotation in family albums," in Proc. 12th Annu. ACM Int. Conf. Multimedia, 2004, pp. 716-723.
12. M. Stricker and M. Orengo, "Similarity of color images," Proc. SPIE Storage and Retrieval for Image and Video Databases, pp. 381-392, 1995.
13. J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu, and R. Zabih, "Image indexing using color correlograms," in Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, 1997, pp. 762-768.
14. H. Yu, M. J. Li, H. J. Zhang, and J. F. Feng, "Color texture moments for content-based image retrieval," in Proc. Int. Conf. Image Processing, 2002, pp. 929-932.
15. Y. Rui, T. S. Huang, M. Ortega, and S. Mehrotra, "Relevance feedback: A power tool for interactive content-based image retrieval," IEEE Trans. Circuits Syst. Video Technol., vol. 8, no. 5, pp. 644-655, May 1998.
16. F. Jing, M. J. Li, H. J. Zhang, and B. Zhang, "A unified framework for image retrieval using keyword and visual features," IEEE Trans. Image Process., vol. 14, no. 7, pp. 979-989, Jul. 2005.
17. Y. Lu, C. Hu, X. Zhu, H. J. Zhang, and Q. Yang, "A unified framework for semantics and feature based relevance feedback in image retrieval systems," in Proc. 8th Annu. ACM Int. Conf. Multimedia, 2000, pp. 31-38.
18. Y. Wu, E. Y. Chang, K. C. C. Chang, and J. R. Smith, "Optimal multimodal fusion for multimedia data analysis," in Proc. 12th Annu. ACM Int. Conf. Multimedia, 2004, pp. 572-579.
19. H. J. Zeng, Q. C. He, Z. Chen, W. Y. Ma, and J. W. Ma, "Learning to cluster web search results," in Proc. 27th Annu. Int. ACM SIGIR Conference on Research and Development in Information Retrieval, 2004, pp. 210-217.