In this paper, we first conceive a new perception of the kernel feature. The kernel subspace methods can be regarded as two independent steps: an explicit kernel feature extraction step and a linear subspace analysis step on the extracted kernel features. The kernel feature vector of an image is composed of dot products between the image and all the training images using nonlinear dot product kernel. Then, based on this perception, we further extend the kernel feature vector of an image to a kernel feature matrix for visual recognition. This extension takes different representation cues of images into account, respectively, while only global average information is used in the traditional kernel methods. From the view of dot product as similarity, this extension means using multiple similarities to measure two images, which is more accordant to human vision. In order to efficiently deal with the problem of numerical computation, a matrix-based kernel discriminant analysis algorithm is employed to learn discriminating kernel features for visual recognition. Experiments on the FERET face database, the COIL-100 object database, and the Wang's nature image database show the advantage of the proposed method.
['Qingshan Liu', 'Hongliang Jin', 'Xiaoou Tang', 'Hanqing Lu', 'Songde Ma']
A new extension of kernel feature and its application for visual recognition