Modeling user feedback using a hierarchical graphical model for interactive image retrieval

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4810 LNCS, Issue , 2007, Pages 18-29

  Guan, Jian ^a   Qiu, Guoping ^a  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

Graphical model; Image retrieval; Image segmentation; Relevance feedback; Semi supervised learning

Indexed keywords

HIERARCHICAL SYSTEMS; IMAGE SEGMENTATION; MATHEMATICAL MODELS; SEMANTICS; SUPERVISED LEARNING;

GRAPHICAL MODEL; RELEVANCE FEEDBACK; SEMI-SUPERVISED LEARNING;

IMAGE RETRIEVAL;

EID: 38349022398     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-77255-2_3     Document Type: Conference Paper

References (22)

1. Borenstein, E., Ullman, S.: Learning to segment. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3024, Springer, Heidelberg (2004)
2. Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images. In: Pro. ICCV 2001 (2001)
3. Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE TPAMI 26, 1124-1137 (2004)
4. Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: image segmentation using expectation-maximization and its application to image querying. IEEE TPAMI 24, 1026-1038 (2002)
5. Cherkassky, B.V., Goldberg, A.V.: On implementing push-relabel method for the maximum flow problem. Algorithmica 19(4), 390-410 (1997)
6. Ford, L.R., Fulkerson, D.R.: Flows in Networks. Princeton University Press, Princeton (1962)
7. Hackbusch, W.: Multi-grid Methods and Applications. Springer, Berlin (1985)
8. Jing, F., Li, M., Zhang, H.J., Zhang, B.: Region-based relevance feedback in image retrieval. IEEE Trans. on Circuits and Systems for Video Technology 14(5), 672-681 (2004)
9. Minka, T.P., Picard, R.W.: Interactive Learning Using A Society of Models. Pattern Recognition 30(4), 565-581 (1997)
10. Qiu, G.: Indexing chromatic and achromatic patterns for content-based colour image retrieval. Pattern Recognition 35, 1675-1686 (2002)
11. Rother, C., Kolmogorov, V., Minka, T., Blake, A.: Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs. In: Proc. CVPR 2006 (2006)
12. Rui, Y., Huang, T.S.: Optimizing Learning in Image Retrieval. In: Proc. CVPR 2000 (2000)
13. Rui, Y., Huang, T.S., Ortega, M., Mehrotra, S.: Relevance feedback: A power tool in interactive content-based image retrieval. IEEE Trans. on Circuits and Systems for Video Technology 8(5), 644-655 (1998)
14. Shi, J., Malik, J.: Normalized cuts and image segmentation. In: Proc. CVPR 1997(1997)
15. Tong, S., Chang, E.Y.: Support vector machine active learning for image retrieval. In: Proc. ACM International Multimedia Conference (2001)
16. Vasconcelos, N., Lippman, A.: Learning from user feedback in image retrieval system. In: Proc. NIPS 1999 (1999)
17. Wang, J., Li, G., Wiederhold, G.: Simplicity: Semantics-sensitive integrated matching for picture libraries. IEEE TPAMI 23, 947-963 (2001)
18. Winn, J., Jojic, N.: LOCUS: Learning Object Classes with Unsupervised Segmentation. In: Proc. ICCV 2005 (2005)
19. Wood, M.E., Campbell, N.W., Thomas, B.T.: Iterative refinement by relevance feedback in content based digital image retrieval. In: Proc. ACM International Multimedia Conference (1998)
20. Yang, M., Guan, J., Qiu, G., Lam, K-M.: Semi-supervised Learning based on Bayesian Networks and Optimization for Interactive Image Retrieval. In: Proc. BMVC 2006
21. Yu, S-X., Shi, J.: Segmentation given partial grouping Constraints. IEEE TPAMI 26(2), 173-183 (2004)
22. Zhou, X.S., Huang, T.S.: Relevance feedback in image retrieval: A comprehensive review. Multimedia Syst. 8(6), 536-544 (2003)