An Effective Image Representation Method Using Kernel Classification

The learning of image representation is always the most important problem in computer vision community. In this paper, we propose a novel image representation method by learning and using kernel classifiers. We firstly train classifiers using the one-against-all rule, then use them classify the candidate images, and finally using the classification responses as the new representations. The Euclidean distance between the classification response vectors are used as the new similarity measure. The experimental results from a large scale image database show that the proposed algorithm can outperform the original feature on image retrieval problem.