A Discussion of Nonlinear Variants of Biased Discriminants for Interactive Image Retrieval

During an interactive image retrieval process with relevance feedback, kernel-based or boosted learning algorithms can provide superior nonlinear modeling capability. In this paper, we discuss such nonlinear extensions for biased discriminants, or BiasMap [1, 2]. Kernel partial alignment is proposed as the criterion for kernel selection. The associated analysis also provides a gauge on relative class scatters, which can guide an asymmetric learner, such as BiasMap, toward better class modeling. We also propose two boosted versions of BiasMap. Unlike existing approach that boosts feature components or vectors to form a composite classifier, our scheme boosts linear BiasMap toward a nonlinear ranker which is more suited for small-sample learning during interactive image retrieval. Experiments on heterogeneous image database retrieval as well as small sample face retrieval are used for performance evaluations.

[1]  Daphne Koller,et al.  Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..

[2]  Marcel Worring,et al.  Interaction in Content-Based Image Retrieval: The Evaluation of the State-of-the-Art Review , 2000, VISUAL.

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

[4]  Thomas S. Huang,et al.  Small sample learning during multimedia retrieval using BiasMap , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[5]  Bir Bhanu,et al.  Active concept learning for image retrieval in dynamic databases , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[6]  Thomas S. Huang,et al.  One-class SVM for learning in image retrieval , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[7]  Paul A. Viola,et al.  Boosting Image Retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[8]  Daphne Koller,et al.  Support Vector Machine Active Learning with Application sto Text Classification , 2000, ICML.

[9]  Wei-Ying Ma,et al.  Learning similarity measure for natural image retrieval with relevance feedback , 2002, IEEE Trans. Neural Networks.

[10]  Pasquale J. Dipillo.  Biased discriminant analysis: Evaluation of the optimum probability of misclassification , 1979 .

[11]  Stan Z. Li,et al.  Extraction of feature subspaces for content-based retrieval using relevance feedback , 2001, MULTIMEDIA '01.

[12]  Bo Zhang,et al.  Support vector machine learning for image retrieval , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[13]  Nicholas R. Howe,et al.  A Closer Look at Boosted Image Retrieval , 2003, CIVR.

[14]  Edward Y. Chang,et al.  Support vector machine active learning for image retrieval , 2001, MULTIMEDIA '01.

[15]  Qi Tian,et al.  Incorporate support vector machines to content-based image retrieval with relevance feedback , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[16]  Yoram Singer,et al.  An Efficient Boosting Algorithm for Combining Preferences by , 2013 .

[17]  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.

[18]  G. Baudat,et al.  Generalized Discriminant Analysis Using a Kernel Approach , 2000, Neural Computation.

[19]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[20]  Gunnar Rätsch,et al.  A Mathematical Programming Approach to the Kernel Fisher Algorithm , 2000, NIPS.

[21]  Qi Tian,et al.  Discriminant-EM algorithm with application to image retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[22]  Murat Kunt,et al.  Content-based retrieval from image databases: current solutions and future directions , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[23]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[24]  Thomas S. Huang,et al.  Exploration of Visual Data , 2003, The Springer International Series in Video Computing.