Online Kernel-Based Multimodal Similarity Learning with Application to Image Retrieval

A challenging problem of image retrieval is the similarity learning between images. To improve similarity search in Content-Based Image Retrieval (CBIR), many studies on distance metric learning have been published. Despite their success, most existing methods are limited in two aspects: (i) they usually attempt to learn a linear distance metric, which limits their capacity of measuring similarity for complex applications; (ii) they are often designed for learning metrics on unique-modal data, which could be suboptimal for similarity learning on multimedia objects with multiple feature representations. To overcome these limitations, in this paper, we investigate the online kernel-based multimodal similarity learning, which aims to integrate multiple kernels for learning multimodal similarity functions, and conduct experiments to evaluate the performance of the proposed method for CBIR on several different image datasets. The experiment results are encouraging and verify the effectiveness and the superiority of the proposed method.