Higher-level feature combination via multiple kernel learning for image classification

Feature combination is an effective way for image classification. Most of the work in this line mainly considers feature combination based on different low-level image descriptors, while ignoring the complementary property of different higher-level image features derived from the same type of low-level descriptor. In this paper, we explore the complementary property of different image features generated from one single type of low-level descriptor for image classification. Specifically, we propose a soft salient coding (SSaC) method, which overcomes the information suppression problem in the original salient coding (SaC) method. We analyse the physical meaning of the SSaC feature and the other two types of image features in the framework of Spatial Pyramid Matching (SPM), and propose using multiple kernel learning (MKL) to combine these features for classification tasks. Experiments on three image databases (Caltech-101, UIUC 8-Sports and 15-Scenes) not only verify the effectiveness of the proposed MKL combination method, but also reveal that collaboration is more important than selection for classification when limited types of image features are employed.