Maximum margin hashing with supervised information

Abstract

Binary code is a kind of special representation of data. With the binary format, hashing framework can be built and a large amount of data can be indexed to achieve fast research and retrieval. Many supervised hashing approaches learn hash functions from data with supervised information to retrieve semantically similar samples. This kind of supervised information can be generated from external data other than pixels. Conventional supervised hashing methods assume a fixed relationship between the Hamming distance and the similar (dissimilar) labels. This assumption leads to too rigid requirement in learning and makes the similar and dissimilar pairs not distinguishable. In this paper, we adopt a large margin principle and define a Hamming margin to formulate such relationship. At the same time, inspired by support vector machine which achieves strong generalization capability by maximizing the margin of its decision surface, we propose a binary hash function in the same manner. A loss function is constructed corresponding to these two kinds of margins and is minimized by a block coordinate descent method. The experiments show that our method can achieve better performance than the state-of-the-art hashing methods.