Improved image representation and sparse representation for image classification

Abstract

It seems that for multiple available images of the same object, the pixel values at the same image position are almost always different, which is especially obvious for the deformable object. This implies that it will be not easy to correctly classify the deformable object. In order to extract salient features of images and improve the performance of image classification, a novel image classification algorithm is proposed in this paper. The algorithm can effectively preserve the large-scale information and global features of the original image, reduce the difference in different images of the same object, and significantly improve the accuracy of image classification. Firstly, the virtual image is generated by the new image representation procedure. Secondly, the image classification algorithm is used to obtain the corresponding classification scores of the original image and the virtual image, respectively. Finally, the ultimate classification score is obtained by a simple and efficient score fusion scheme. A large number of experiments on three widely used image databases show that the proposed algorithm outperforms other state-of-the-art algorithms in classification accuracy. At the same time, the algorithm has the advantages of simple implementation and high computational efficiency.