Sciweavers

ACCV
2006
Springer

Multiple Similarities Based Kernel Subspace Learning for Image Classification

13 years 9 months ago
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.
Wang Yan, Qingshan Liu, Hanqing Lu, Songde Ma
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2006
Where ACCV
Authors Wang Yan, Qingshan Liu, Hanqing Lu, Songde Ma
Comments (0)