Hamming embedding similarity-based image classification

In this paper, we propose a novel image classification framework based on patch matching. More precisely, we adapt the Hamming Embedding technique, first introduced for image search to improve the bag-of-words representation. This matching technique allows the fast comparison of descriptors based on their binary signatures, which refines the matching rule based on visual words and thereby limits the quantization error. Then, in order to allow the use of efficient and suitable linear kernel-based SVM classification, we propose a mapping method to cast the scores output by the Hamming Embedding matching technique into a proper similarity space. Comparative experiments of our proposed approach and other existing encoding methods on two challenging datasets PASCAL VOC 2007 and Caltech-256, report the interest of the proposed scheme, which outperforms all methods based on patch matching and even provide competitive results compared with the state-of-the-art coding techniques.