Relevance feedback approach for image retrieval combining support vector machines and adapted Gaussian mixture models

IET Image Processing

Volumn 5, Issue 6, 2011, Pages 531-540

  Marakakis, A ^a   Siolas, G ^a   Galatsanos, N ^b   Likas, A ^c   Stafylopatis, A ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

^b UNIVERSITY OF PATRAS   (Greece)

^c UNIVERSITY OF IOANNINA   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

COMMON MODELS; CONTENT-BASED IMAGE RETRIEVAL; GAUSSIAN MIXTURE MODEL; GAUSSIAN MIXTURES; IMAGE DATABASE; IMAGE REPRESENTATIONS; KERNEL FUNCTION; KULLBACK-LEIBLER; NEGATIVE EXAMPLES; NUMERICAL EXPERIMENTS; RELEVANCE FEEDBACK;

FEEDBACK; IMAGE RETRIEVAL; PROBABILITY DISTRIBUTIONS;

SUPPORT VECTOR MACHINES;

EID: 79960669386     PISSN: 17519659     EISSN: None     Source Type: Journal    
DOI: 10.1049/iet-ipr.2009.0402     Document Type: Article

References (43)

1. Rui, Y., Huang, T.S., and Chang, S.F.: 'Image retrieval: current techniques, promising directions, and open issues', J. Vis. Commun. Image Represent., 1999, 10, p. 39-6210.1006/jvci.1999.0413 1047-3203
2. Datta, R., Li, J., and Wang, J.Z.: 'Content-based image retrieval: approaches and trends of the new age', Proc. 7th ACM SIGMM Int. Workshop on Multimedia Information Retrieval (MIR'05), 2005, p. 253-262
3. Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., and Jain, R.: 'Content-based image retrieval at the end of the early years', IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (12), p. 1349-138010.1109/34.895972 0162-8828
4. Vasconcelos, N.: 'Minimum probability of error image retrieval', IEEE Trans. Signal Process., 2004, 52, (8), p. 2322-233610.1109/TSP.2004.831125 1053-587X
5. Manjunath, B.S., Ohm, J.R., Vasudevan, V.V., and Yamada, A.: 'Color and texture descriptors', IEEE Trans. Circuits Syst. Video Technol., 2001, 11, (6), p. 703-71510.1109/76.927424 1051-8215
6. Da Cunha, A.L., Jianping, Z., and Do, M.N.: 'The nonsubsampled contourlet transform: theory, design, and applications', IEEE Trans. Image Process., 2006, 15, (10), p. 3089-310110.1109/TIP.2006.877507 1057-7149
7. Lowe, D.G.: 'Distinctive image features from scale-invariant keypoints', Int. J. Comput. Vis., 2004, 60, (2), p. 91-11010.1023/B:VISI.0000029664.99615.94 0920-5691
8. Burghouts, G.J., and Geusebroek, J.M.: 'Performance evaluation of local colour invariants', Comput. Vis. Image Underst., 2009, 113, p. 48-6210.1016/j.cviu.2008.07.003 1077-3142
9. Abdel-Hakim, A.E., and Farag, A.A.: 'CSIFT: a SIFT descriptor with color invariant characteristics', Proc. IEEE Computer Social Conf. on Computer Visual Pattern Recognition, 2006, vol. 2, p. 1978-1983
10. Ke, Y., and Sukthankar, R.: 'PCA-SIFT: a more distinctive representation for local image descriptors', Proc. IEEE Computer Social Conf. on Computer Visual Pattern Recognition, 2004, vol. 2, p. 506-513
11. Su, Z., Zhang, H., Li, S., and Ma, S.: 'Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces, and progressive learning', IEEE Trans. Image Process., 2003, 12, (8), p. 924-93710.1109/TIP.2003.815254 1057-7149
12. Hsu, C.T., and Li, C.Y.: 'Relevance feedback using generalized Bayesian framework with region-based optimization learning', IEEE Trans. Image Process., 2005, 14, (10), p. 1617-163110.1109/TIP.2005.852202 1057-7149
13. Tong, S., and Chang, E.: 'Support vector machine active learning for image retrieval', Proc. ACM Int. Conf. on Multimedia, 2001, p. 107-118
14. Kherfi, M.L., Ziou, D., and Bernardi, A.: 'Combining positive and negative examples in relevance feedback for content-based image retrieval', J. Vis. Commun. Image Represent., 2003, 14, p. 428-45710.1016/S1047-3203(03)00043-9 1047-3203
15. Marakakis, A., Galatsanos, N., Likas, A., and Stafylopatis, A.: 'Probabilistic relevance feedback approach for content-based image retrieval based on Gaussian mixture models', IET Image Process., 2009, 3, (1), p. 10-2510.1049/iet-ipr:20080012
16. Guo, G.D., Jain, A.K., Ma, W.Y., and Zhang, H.J.: 'Learning similarity measure for natural image retrieval with relevance feedback', IEEE Trans. Neural Netw., 2002, 13, (4), p. 811-82010.1109/TNN.2002.1021882 1045-9227
17. Jing, F., Li, M., Zhang, H.J., and Zhang, B.: 'An efficient and effective region-based image retrieval framework', IEEE Trans. Image Process., 2004, 13, (5), p. 699-70910.1109/TIP.2004.826125 1057-7149
18. Jiang, W., Er, G., Dai, Q., and Gu, J.: 'Similarity-based online feature selection in content-based image retrieval', IEEE Trans. Image Process., 2006, 15, (3), p. 702-71210.1109/TIP.2005.863105 1057-7149
19. Vasconcelos, N., Ho, P., and Moreno, P.: 'The Kullback-Leibler kernel as a framework for discriminant and localized representations for visual recognition', European Conf. on Computer Vision, 2004, p. 430-441
20. 'Improving 'bag-of-keypoints' image categorisation', Technical report, University of Southampton, 2005
21. Liu, Y., and Perronnin, F.: 'A similarity measure between unordered vector sets with application to image categorization', p. 1-8 IEEE Conf. on Computer Vision Pattern Recognition, 2008
22. Ishikawa, Y., Subramanya, R., and Faloutsos, C.: 'MindReader: querying databases through multiple examples', Proc. Conf. on Very Large Data Bases, 1998
23. Qian, F., Li, M., Zhang, L., Zhang, H.J., and Zhang, B.: 'Gaussian mixture model for relevance feedback in image retrieval', Proc. IEEE ICME, August 2002
24. Kherfi, M.L., and Ziou, D.: 'Relevance feedback for CBIR: a new approach based on probabilistic feature weighting with positive and negative examples', IEEE Trans. Image Process., 2006, 15, (4), p. 1017-103010.1109/TIP.2005.863969 1057-7149
25. Vasconcelos, N., and Lippman, A.: 'Learning over multiple temporal scales in image databases', European Conf. on Computer Vision, Dublin, Ireland 2000
26. Vasconcelos, N., and Lippman, A.: 'Learning from user feedback in image retrieval systems', Proc. Advances in Neural Information Processing Systems (NIPS'99), 1999, p. 977-986
27. Carson, C., Belongie, S., Greenspan, H., and Malik, J.: 'Blobworld: Image segmentation using expectation-maximization and its application to image querying', IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (8), p. 1026-103810.1109/TPAMI.2002.1023800 0162-8828
28. Bishop, C.M.: 'Neural networks for pattern recognition', (Oxford University Press Inc., New York 1995)
29. Mclachlan, G.M., and Peel, D.: 'Finite mixture models', (John Wiley & Sons Inc., New York 2001)
30. Goldberger, J., Gordon, S., and Greenspan, H.: 'Unsupervised image-set clustering using an information theoretic framework', IEEE Trans. Image Process., 2006, 15, (2), p. 449-45810.1109/TIP.2005.860593 1057-7149
31. Perronnin, F., Dance, C., Csurka, G., and Bressan, M.: 'Adapted vocabularies for generic visual categorization', Proc. European Conf. on Computer Vision, Springer Verlag, 2006
32. Reynolds, D., Quatieri, T., and Dunn, R.: 'Speaker verification using adapted Gaussian mixture models', Digit. Signal Process., 2000, 10, (1-3), p. 19-4110.1006/dspr.1999.0361 1051-2004
33. Greenspan, H., Dvir, G., and Rubner, Y.: 'Context-dependent segmentation and matching in image databases', Comput. Vis. Image Underst., 2004, 93, p. 86-10910.1016/j.cviu.2003.08.004 1077-3142
34. Vasconcelos, N.: 'On the efficient evaluation of probabilistic similarity functions for image retrieval', IEEE Trans. Inf. Theory, 2004, 50, (7), p. 1482-149610.1109/TIT.2004.830760 0018-9448
35. Goldberger, J., Gordon, S., and Greenspan, H.: 'An efficient image similarity measure based on approximations of KL-divergence between two Gaussian mixtures', Int. Conf. Computer Vision, 2003, 1, p. 487-493
36. Gauvain, J.-L., and Lee, C.-H.: 'Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains', IEEE Trans. Speech Audio Process., 1994, 2, (2), p. 291-29810.1109/89.279278 1063-6676
37. Mikolajczyk, K., and Schmid, C.: 'A performance evaluation of local descriptors', IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, (10), p. 1615-163010.1109/TPAMI.2005.188 0162-8828
38. Bishop, C.M.: 'Pattern recognition and machine learning', (Springer 2006)
39. Homepage of Jan-Mark Geusebroek. http://staff.science.uva.nl/~mark/downloads.html
40. Vlassis, N., and Likas, A.: 'A greedy EM algorithm for Gaussian mixture learning', Neural Process. Lett., 2002, 15, p. 77-87
41. 'Local features and kernels for classification of texture and object categories: an in-depth study', INRIA, 2005
42. LIBSVM - A Library for Support Vector Machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm/
43. Microsoft Research Cambridge Object Recognition Image Database, version 1.0. http://research.microsoft.com/research/downloads/Details/b94de342-60dc-45d0-830b-9f6eff91b301/Details.aspx