Image Retrieval using Long-Term Semantic Learning

The automatic computation of features for content-based image retrieval still has difficulties to represent the concepts the user has in mind. Whenever an additional learning strategy (such as relevance feedback) can improve the results of the search, the system performances still depend on the representation of the image collection. We introduce in this paper a supervised optimization of a set of feature vectors. According to an incomplete set of partial labels, the method improves the representation of the image collection, even if the size, the number, and the structure of the concepts are unknown. Experiments have been carried out on a large general database in order to validate our approach.

[1]  Lei Guo,et al.  A memorization learning model for image retrieval , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[2]  Edward Y. Chang,et al.  Statistical learning for effective visual information retrieval , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[3]  Si Wu,et al.  Improving support vector machine classifiers by modifying kernel functions , 1999, Neural Networks.

[4]  N. Cristianini,et al.  On Kernel-Target Alignment , 2001, NIPS.

[5]  Douglas R. Heisterkamp.  Building a latent semantic index of an image database from patterns of relevance feedback , 2002, Object recognition supported by user interaction for service robots.

[6]  Jing Peng,et al.  Adaptive quasiconformal kernel metric for image retrieval , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[7]  Nello Cristianini,et al.  Learning the Kernel Matrix with Semidefinite Programming , 2002, J. Mach. Learn. Res..

[8]  Matthieu Cord,et al.  Long-term similarity learning in content-based image retrieval , 2002, Proceedings. International Conference on Image Processing.

[9]  Matthieu Cord,et al.  RETIN AL: an active learning strategy for image category retrieval , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[10]  Thorsten Joachims,et al.  Learning a Distance Metric from Relative Comparisons , 2003, NIPS.

[11]  Matthieu Cord,et al.  Semantic kernel learning for interactive image retrieval , 2005, IEEE International Conference on Image Processing 2005.

[12]  Thierry Pun,et al.  Long-Term Learning from User Behavior in Content-Based Image Retrieval , 2000 .

[13]  Michael I. Jordan,et al.  Distance Metric Learning with Application to Clustering with Side-Information , 2002, NIPS.

[14]  John R. Smith,et al.  Over-complete representation and fusion for semantic concept detection , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..