Multi-view Latent Hashing for Efficient Multimedia Search

Hashing techniques have attracted broad research interests in recent multimedia studies. However, most of existing hashing methods focus on learning binary codes from data with only one single view, and thus cannot fully utilize the rich information from multiple views of data. In this paper, we propose a novel unsupervised hashing approach, dubbed multi-view latent hashing (MVLH), to effectively incorporate multi-view data into hash code learning. Specifically, the binary codes are learned by the latent factors shared by multiple views from an unified kernel feature space, where the weights of different views are adaptively learned according to the reconstruction error with each view. We then propose to solve the associate optimization problem with an efficient alternating algorithm. To obtain high-quality binary codes, we provide a novel scheme to directly learn the codes without resorting to continuous relaxations, where each bit is efficiently computed in a closed form. We evaluate the proposed method on several large-scale datasets and the results demonstrate the superiority of our method over several other state-of-the-art methods.