Dynamic Multi-View Hashing for Online Image Retrieval

Advanced hashing technique is essential to facilitate effective organization and retrieval over online image collections, where the contents are frequently changed. Traditional multi-view hashing methods based on batch-based learning generally leads to very expensive updating cost. Meanwhile, existing online hashing methods mainly focus on single-view data and thus can not achieve promising performance when searching real online images, which are multiple view based data. In this paper, we propose dynamic multi-view hashing (DMVH), which can adaptively augment hash codes according to dynamic changes of image. Meanwhile, DMVH leverages online learning to generate hash codes. It can increase the code length when current code is not able to represent new images effectively. Moreover, to gain further improvement on overall performance, each view is assigned with a weight, which can be efficiently updated during the online learning process. In order to avoid the frequent updating of code length and view weights, an intelligent buffering scheme is also specifically designed to preserve significant data to maintain good effectiveness of DMVH. Experimental results on two real-world image datasets demonstrate superior performance of DWVH over several state-of-the-art hashing methods.