Multiple similarities based kernel subspace learning for image classification

Authors:
Wang Yan;Qingshan Liu;Hanqing Lu;Songde Ma
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Venue:
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Year:
2006

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Abstract

In this paper, we propose a new method for image classification, in which matrix based kernel features are designed to capture the multiple similarities between images in different low-level visual cues. Based on the property that dot product kernel can be regarded as a similarity measure, we apply kernel functions to different low-level visual features respectively to measure the similarities between two images, and obtain a kernel feature matrix for each image. In order to deal with the problems of over fitting and numerical computation, a revised version of Two-Dimensional PCA algorithm is developed to learn intrinsic subspace of matrix features for classification. Extensive experiments on the Corel database show the advantage of the proposed method.