Combining visual dictionary, kernel-based similarity and learning strategy for image category retrieval

Computer Vision and Image Understanding

Volumn 110, Issue 3, 2008, Pages 403-417

  Gosselin, Philippe Henri ^a   Cord, Matthieu ^b   Philipp Foliguet, Sylvie ^a  

^a Centre national de la recherche scientifique   (France)

^b UNIVERSITÉ PIERRE ET MARIE CURIE   (France)

Author keywords

Kernel functions; Machine learning; Multimedia retrieval; Quantization

Indexed keywords

DATABASE SYSTEMS; FUNCTIONS; LEARNING SYSTEMS; SEMANTICS;

KERNEL FUNCTIONS; MULTIMEDIA RETRIEVAL; QUANTIZATION;

IMAGE RETRIEVAL;

EID: 43049181309     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2007.09.018     Document Type: Article

References (52)

1. Y. Rui, T. Huang, S. Mehrotra, M. Ortega, A relevance feedback architecture for content-based multimedia information retrieval systems, in: IEEE Workshop on Content-Based Access of Image and Video Libraries, 1997, pp. 92-89.
2. Santini S., Gupta A., and Jain R. Emergent semantics through interaction in image databases. IEEE Transactions on Knowledge and Data Engineering 13 3 (2001) 337-351
3. A. Mojsilovic, B. Rogowitz, Capturing image semantics with low-level descriptors, in: International Conference in Image Processing (ICIP'01), vol. 1, Thessaloniki, Greece, 2001, pp. 18-21.
4. Schmid C. Weakly supervised learning of visual models and its application to content-based retrieval. International Journal of Computer Vision 56 1 (2004) 7-16. http://lear.inrialpes.fr/pubs/2004/Sch04
5. Chen Y., and Wang J. Image categorization by learning and reasoning with regions. International Journal on Machine Learning Research 5 (2004) 913-939
6. N. Vasconcelos, M. Kunt, Content-based retrieval from image databases: current solutions and future directions, in: International Conference in Image Processing, vol. 3, Thessaloniki, Greece, 2001, pp. 6-9.
7. R. Veltkamp, M. Tanase, Content-based image retrieval systems: a survey, Technical report UU-CS-2000-34, Department of Computing Science, Utrecht University, October 2000.
8. Y. Ishikawa, R. Subramanya, C. Faloutsos, MindReader: querying databases through multiple examples, in: Proceedings of the 24th International Conference on Very Large Data Bases, VLDB, 1998, pp. 218-227.
9. Y. Rui, T. Huang, Optimizing learning in image retrieval, in: Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, Hilton Head, SC, 2000, pp. 236-243.
10. Sebe N., Cohen I., Garg A., and Huang T. Machine Learning in Computer Vision (2005), Springer-Verlag, Berlin. 1-4020-3274-9
11. Tong S., and Koller D. Support vector machine active learning with application to text classification. Journal of Machine Learning Research 2 (2001) 45-66
12. Chapelle O., Haffner P., and Vapnik V. Svms for histogram based image classification. IEEE Transactions on Neural Networks 10 (1999) 1055-1064
13. Cord M., Gosselin P., and Philipp-Foliguet S. Stochastic exploration and active learning for image retrieval. Image and Vision Computing 25 (2006) 14-23
14. Fayyad U.M., Piatetsky-Shapiro G., Smyth P., and Uthurusamy R. Advances in Knowledge Discovery and Data Mining (1996), AAAI/MIT Press
15. J. Smith, S. Chang, VisualSEEK: a fully automated content-based image query system, in: ACM Multimedia Conference, Boston, USA, 1996, pp. 87-98.
16. Manjunath B., Ohm J.-R., Vasudevan V., and Yamada A. Color and texture descriptors. IEEE Transactions on Circuits and Systems for Video Technology 11 6 (2001) 703-715
17. Linde Y., Buzo A., and Gray R. An algorithm for vector quantizer design. IEEE Transaction on Communication 28 (1980) 84-94
18. Patanè G., and Russo M. The enhanced LBG algorithm. Neural Networks 14 9 (2001) 1219-1237
19. Fournier J., Cord M., and Philipp-Foliguet S. Retin: a content-based image indexing and retrieval system. Pattern Analysis and Applications Journal, Special issue on image indexation 4 2/3 (2001) 153-173
20. Swain M., and Ballard D. Color indexing. International Journal of Computer Vision 7 1 (1991) 11-32
21. Kullback S. Information Theory and Statistics (1959), Wiley, New York
22. J. Puzicha, T. Hofmann, J. Buhmann, Non-parametric similarity measures for unsupervised texture segmentation and image retrieval, in: IEEE Conference on Computer Vision and Pattern Recognition, 1997, pp. 267-272.
23. I. Sethi, N. Patel, Statistical approach to scene change detection, in: Storage and Retrieval for Image and Video Databases, 1995, pp. 329-338.
24. M. Stricker, M. Orengo, Similarity of color images, in: SPIE, Storage and Retrieval for Image Video Databases III, vol. 2420, 1995, pp. 381-392.
25. Y. Rubner, Perceptual metrics for image database navigation, Ph.D. thesis, Stanford University, May 1999.
26. Vapnik V. Statistical Learning Theory (1998), Wiley-Interscience, New York
27. Shawe-Taylor J., and Cristianini N. Kernel methods for Pattern Analysis (2004), Cambridge University Press, Cambridge. 0-521-81397-2
28. E. Chang, B.T. Li, G. Wu, K. Goh, Statistical learning for effective visual information retrieval, in: IEEE International Conference on Image Processing, Barcelona, Spain, 2003, pp. 609-612.
29. S. Aksoy, R. Haralick, F. Cheikh, M. Gabbouj, A weighted distance approach to relevance feedback, in: IAPR International Conference on Pattern Recognition, vol. IV, Barcelona, Spain, 2000, pp. 812-815.
30. Peng J., Bhanu B., and Qing S. Probabilistic feature relevance learning for content-based image retrieval. Computer Vision and Image Understanding 75 1-2 (1999) 150-164
31. N. Doulamis, A. Doulamis, A recursive optimal relevance feedback scheme for cbir, in: International Conference in Image Processing (ICIP'01), Thessaloniki, Greece, 2001.
32. J. Fournier, M. Cord, S. Philipp-Foliguet, Back-propagation algorithm for relevance feedback in image retrieval, in: International Conference in Image Processing (ICIP'01), vol. 1, Thessaloniki, Greece, 2001, pp. 686-689.
33. N. Vasconcelos, Bayesian models for visual information retrieval, Ph.D. thesis, Massachusetts Institute of Technology, 2000.
34. Berrani S.-A., Amsaleg L., and Gros P. Recherche approximative de plus proches voisins: application la reconnaissance d'images par descripteurs locaux. Technique et Science Informatiques 22 9 (2003) 1201-1230
35. B.L. Saux, Classification non exclusive et personnalisation par apprentissage: application à la navigation dans les bases d'images, Ph.D. thesis, INRIA, 2003.
36. Najjar N., Cocquerez J., and Ambroise C. Feature selection for semi supervised learning applied to image retrieval. IEEE ICIP vol. 2 (2003), Barcelena, Spain 559-562
37. X. Zhu, Z. Ghahramani, J. Lafferty, Semi-supervised learning using gaussian fields and harmonic functions, in: International Conference on Machine Learning, 2003.
38. P. Gosselin, M. Cord, RETIN AL: an active learning strategy for image category retrieval, in: IEEE International Conference on Image Processing, vol. 4, Singapore, 2004, pp. 2219-2222.
39. T. Joachims, Transductive inference for text classification using support vector machines, in: Proceedings of the 16th International Conference on Machine Learning, Morgan Kaufmann, San Francisco, CA, 1999, pp. 200-209.
40. S. Mika, G. Rätsch, J. Weston, B. Schölkopf, K.-R. Müller, Fisher discriminant analysis with kernels, in: Y.-H. Hu, J. Larsen, E. Wilson, S. Douglas (Eds.), Neural Networks for Signal Processing IX, IEEE, 1999, pp. 41-48, URL: citeseer.ist.psu.edu/mika99fisher.html.
41. N. Roy, A. McCallum, Toward optimal active learning through sampling estimation of error reduction, in: International Conference on Machine Learning, 2001.
42. S. Tong, E. Chang, Support vector machine active learning for image retrieval, in: ACM Multimedia, 2001, pp. 107-118.
43. Cohn D. Active learning with statistical models. Journal of Artificial Intelligence Research 4 (1996) 129-145
44. P. Gosselin, M. Cord, Precision-oriented active selection for interactive image retrieval, in: IEEE International Conference on Image Processing, Atlanta, GA, USA, 2006.
45. K. Brinker, Incorporating diversity in active learning with support vector machines, in: International Conference on Machine Learning, 2003, pp. 59-66.
46. H. Müller, W. Müler, D.M. Squire, S. Marchand-Maillet, T. Pun, Long-term learning from user behavior in content-based image retrieval, Technical report, Computer Vision Group, University of Geneva, Switzerland, 2000.
47. D.R. Heisterkamp, Building a latent semantic index of an image database from patterns of relevance feedback, in: International Conference on Pattern Recognition, vol. 4, Quebec City, Canada, 2002, pp. 132-137.
48. Cristianini N., Shawe-Taylor J., Elisseff A., and Kandola J. On kernel target alignment. Neural Information Processing Systems (2001), Vancouver, Canada
49. G.R.G. Lanckriet, N. Cristianini, N. Bartlett, L. El Ghaoui, M.I. Jordan, Learning the kernel matrix with semi-definite programming, in: International Conference on Machine Learning, Sydney, Australia, 2002.
50. Fournier J., and Cord M. Long-term similarity learning in content-based image retrieval. International Conference in Image Processing (ICIP) (2002), Rochester, New York, USA
51. Xing E.P., Ng A.Y., Jordan M.I., and Russell S. Distance metric learning, with application to clustering with side-information. Neural Information Processing Systems (2002), Vancouver, British Columbia
52. M. Schultz, T. Joachims, Learning a distance metric from relative comparisons, in: Neural Information Processing Systems, 2003.