Ego-similarity measurement for relevance feedback

Expert Systems with Applications

Volumn 37, Issue 1, 2010, Pages 871-877

  Lee, Chu Hui ^a   Lin, Meng Feng ^a  

^a CHAOYANG UNIVERSITY OF TECHNOLOGY   (Taiwan)

Author keywords

Content based image retrieval; Image database; Relevance feedback

Indexed keywords

ACTIVE FIELD; ADAPTIVE WEIGHTS; CONTENT-BASED IMAGE RETRIEVAL; DATABASE IMAGES; IMAGE DATABASE; INTERACTIVE TECHNIQUES; QUERY IMAGES; RELEVANCE FEEDBACK; RELEVANT FEEDBACK; RETRIEVAL EFFECTIVENESS; SIMILARITY MEASUREMENTS; USER FEEDBACK;

CONTENT BASED RETRIEVAL; DATABASE SYSTEMS; INFORMATION RETRIEVAL;

FEEDBACK;

EID: 70349445436     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.05.101     Document Type: Article

References (29)

1. Chen Z., and Zhu B. Some formal analysis of Rocchio's similarity-based relevance feedback algorithm. Information Retrieval 5 1 (2002) 61-86
2. Cox I.J., Miller M.L., Minka T.P., Papathoman T.V., and Yianilos P.V. The Bayesian image retrieval system: PicHunter theory, implementation and psychological experiments. IEEE Transactions on Image Processing 9 1 (2000) 20-37
3. Doulamis N., and Doulamis A. Evaluation of relevance feedback schemes in content-based in retrieval systems. Signal Processing Image Communication 21 4 (2006) 334-357
4. Flickner M., Sawhney H., Niblack W., Ashley J., Huang Q., Dom B., et al. Querying by image and video content: The QBIC system. IEEE Transactions on Computers 28 9 (1995) 23-32
5. Grigorova A., De Natale F.G.B., Dagli C., and Huang T.S. Content-based image retrieval by feature adaptation and relevance feedback. IEEE Transactions on Multimedia 9 6 (2007) 1183-1192
6. Gupta A., and Jain R. Visual information retrieval. Communications of the ACM 40 5 (1997) 70-79
7. Huang, T. S., Zhou, X. S., Nakazato, M., Wu, Y., & Cohen, I. (2002). Learning in content-based image retrieval. In Proceedings of the 2nd International Conference on Development and Learning (pp. 155-162).
8. Huang P.W., and Lee C.H. Image database design based on 9D-SPA representation for spatial relations. IEEE Transactions on Knowledge and Data Engineering 16 12 (2004) 1486-1496
9. Irwin K., and Zhong J. Integrated probability function and its application to content-based image retrieval by relevance feedback. Pattern Recognition 36 9 (2003) 2177-2186
10. James, Z. (2007). Wang's Research Group, retrieval date on 2007/12/19 from .
11. Kherfi M.L., and Ziou D. Relevance Feedback for CBIR: A new approach based on probabilistic feature weighting with positive and negative examples. IEEE Transactions on Image Processing 15 4 (2006) 1017-1030
12. Laaksonen J., Koskela M., Laakso S., and Oja E. Self-organising maps as a relevance feedback technique in content-based image retrieval. Pattern Analysis& Applications 4 2-3 (2001) 140-152
13. Lee, C. H.,& Lin, M. F. (2008). Adaptive similarity measurement using relevance feedback. In IEEE 8th International Conference on Computer and Information Technology Workshops (pp. 314-318).
14. Liu Y., Zhang D., Lu G., and Ma W.Y. A survey of content-based image retrieval with high-level semantics. Pattern Recognition 40 1 (2007) 262-282
15. Ma W.Y., and Manjunath B.S. Netra: A toolbox for navigating large image databases. Multimedia Systems 7 3 (1999) 184-198
16. Pentland A., Picard R.W., and Scaroff S. Photobook: Content-based manipulation for image databases. International Journal of Computer Vision 18 3 (1996) 233-254
17. Rui Y., Huang T.S., Ortega M., and Mehrotra S. Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Transactions on Circuits and Systems for Video Technology 8 5 (1998) 644-655
18. Saha S.K., Das A.K., and Chanda B. Image retrieval based on indexing and relevance feedback. Pattern Recognition Letters 28 3 (2007) 357-366
19. Smith, J. R.,& Chang, S. F. (1997). VisualSeek: A fully automatic content-based query system. In Proceedings of the Fourth ACM International Conference on Multimedia (pp. 87-98).
20. Subramanyam Rallabandi V.P., and Sett S.K. Knowledge-based image retrieval system. Knowledge-Based Systems 21 2 (2008) 89-100
21. Tai, X., Wu, C., Ren, F., & Kita, K. (2006). Image retrieval based on color and texture. In Proceedings of the Fifth Mexican International Conference on Artificial Intelligence (pp. 111-120).
22. Wang, B., Zhang, X., & Li, N. (2006). Relevance feedback technique for content-based image retrieval using neural network learning. In International Conference on Machine Learning and Cybernetics (pp. 3692-3696).
23. Wang J.Z., Li J., and Wiederhold G. SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence 23 9 (2001) 947-963
24. Wong K.M., Cheung K.W., and Po L.M. MIRROR: An interactive content-based image retrieval system. IEEE International Symposium on Circuits and Systems 2 (2005) 1541-1544
25. Yin P.Y., and Li S.H. Content-based image retrieval using association rule mining with soft relevance feedback. Journal of Visual Communication and Image Representation 17 5 (2006) 1108-1125
26. Yoo H.W. Retrieval of movie scenes by semantic matrix and automatic feature weight update. Expert Systems with Applications 34 4 (2008) 2382-2395
27. Yoo H.W., Jang D.S., Jung S.H., Park J.H., and Song K.S. Visual information retrieval system via content-based approach. Pattern Recognition 35 3 (2002) 749-769
28. Yoo H.W., Park H.S., and Jang D.S. Expert system for color image retrieval. Expert Systems with Applications 28 2 (2005) 347-357
29. Zhang R., and Zhang Z. BALAS: Empirical Bayesian learning in the relevance feedback for image retrieval. Image and Vision Computing 24 3 (2006) 211-223