Similarity-based image classification via kernelized sparse representation

We consider the image classification problem based on the similarities between images. The choice of the similarity is related to the particular applications, and it could be based on color, texture, bag-of-features, or even more complex kernels. As long as the pair-wise similarity matrix is transformed into a positive semidefinite one, the similarities of images could be treated as kernels. This transformation makes it possible for kernel methods to solve the similarity-based image classification problem. In this paper, we propose a novel kernelized classification framework based on sparse representation. This new framework casts the classification as finding a sparse linear representation of test image with respect to training images. Unlike the former works, we do this sparse coding procedure through a proposed kernelized orthogonal matching pursuit algorithm, which is performed in inner product space rather than Euclidean space. Through a proper choice of the similarity function, the proposed approach can be applied to diverse image classification problems. Comparative experiments between the proposed method and other existing methods, on two real datasets (Caltech-101 and Face Rec) show that our method performed better.