Multiple Similarities Based Kernel Subspace Learning for Image Classification

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.

• Type: Conference
• Year: 2006
• Where: ACCV
• Authors: Wang Yan, Qingshan Liu, Hanqing Lu, Songde Ma
• Keywords: ACCV 2006, Computer Vision, Kernel Feature, Kernel Feature Matrix, Matrix Based Kernel