Feature combination with Multi-Kernel Learning for fine-grained visual classification

This paper addresses the problem of fine-grained recognition in which local, mid-level features are used for classification. We propose to use the Multi-Kernel Learning framework to learn the relative importance of the features and to select optimal features with regards to the classification performance, in a principled way. Our results show improved classification results on common benchmarks for fine-grained classification, as compared to the best prior state-of-the-art methods. The proposed learning-based combination method also improves the concatenation combination approach which has been the standard practice in combining features so far.