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.
Multiple Similarities Based Kernel Subspace Learning for Image Classification
Computer Vision – ACCV 2006 ; 1 ; 244-253
Lecture Notes in Computer Science ; 3852 , 1
2006-01-01
10 pages
Article/Chapter (Book)
Electronic Resource
English
Principal Component Analysis , Training Image , Kernel Principal Component Analysis , Local Preserve Projection , Kernel Feature Computer Science , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Artificial Intelligence (incl. Robotics) , Algorithm Analysis and Problem Complexity
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