Discrete Multi-view Hashing for Effective Image Retrieval

Recently, hashing techniques have witnessed an increase in popularity due to their low storage cost and high query speed for large scale data retrieval task, e.g., image retrieval. Many methods have been proposed; however, most existing hashing techniques focus on single view data. In many scenarios, there are multiple views in data samples. Thus, those methods working on single view can not make full use of rich information contained in multi-view data. Although some methods have been proposed for multi-view data; they usually relax binary constraints or separate the process of learning hash functions and binary codes into two independent stages to bypass the obstacle of handling the discrete constraints on binary codes for optimization, which may generate large quantization error. To consider these problems, in this paper, we propose a novel hashing method, i.e., Discrete Multi-view Hashing (DMVH), which can work on multi-view data directly and make full use of rich information in multi-view data. Moreover, in DMVH, we optimize discrete codes directly instead of relaxing the binary constraints so that we could obtain high-quality hash codes. Simultaneously, we present a novel approach to construct similarity matrix, which can not only preserve local similarity structure, but also keep semantic similarity between data points. To solve the optimization problem in DMVH, we further propose an alternate algorithm. We test the proposed model on three large scale data sets. Experimental results show that it outperforms or is comparable to several state-of-the-arts.