Supervised hashing with adaptive discrete optimization for multimedia retrieval

Hashing techniques show significant advantage in dealing with enormous high-dimensional image and multimedia data. Specifically, learning based hashing methods attract a lot of attention from researchers thanks to its great performance in image retrieval. But discrete constraint problem of learning based hashing methods makes the optimization extremely difficult, which can be shown to be NP hard. Thus, most of learning based hashing methods relax the constraint and get a suboptimal result. Recently, some researchers propose discrete optimization hashing techniques to learn hash bits without any relaxation and achieve promising results. But, discrete optimization hashing method like Supervised Discrete Hashing (SDH) roughly renews all binary codes and leads to a time-consuming problem. In this paper, we propose an adaptive discrete cyclic coordinate descent (ACC) method to effectively solve discrete optimization problem. The specific objective of our study is to boost the efficiency of discrete hash optimization with equivalent performance. We evaluate the proposed method on image and multimedia databases: CIFAR-10, NUS-WIDE and MIRFLickr-25k and show that our method achieves speed-up over compared the state-of-the-art methods, while having on-par and in some cases even better performance.