Exploiting score distribution for heterogenous feature fusion in image classification

Over the past decades, features generated by different models have been designed to describe various aspects of object. To connect the complementary information and represent the data properly, effective heterogeneous feature fusion methods are required. Multiple kernel learning (MKL) methods are widely adopted to learn the feature weights and to fuse features on score-level. In this paper, we exploit score distribution to address the feature fusion problem and propose a novel method named score-distribution MKL (SD-MKL) for image classification. Different from existing MKL methods, SD-MKL uses weights which are learned from score curves as a constraint on the weights of kernels. It contains two stages in off-line part: (1) independent data is used to construct reference curves according to classes and feature type; (2) samples and corresponding score-distribution weights are put into multi-kernel support vector machine (MKSVM) to learn feature weights. Our experimental results demonstrate the effect of exploiting score-distribution information on two datasets, which significantly benefits the performance of image classification.