Compact kernel hashing with multiple features

Hashing methods, which generate binary codes to preserve certain similarity, recently have become attractive in many applications like large scale visual search. However, most of state-of-the-art hashing methods only utilize single feature type, while combining multiple features has been proved very helpful in image search. In this paper we propose a novel hashing approach that utilizes the information conveyed by different features. The multiple feature hashing can be formulated as a similarity preserving problem with optimal linearly-combined multiple kernels. Such formulation is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also helpful to achieve fast training and search. We present an efficient alternating optimization to learn the hashing functions and the optimal kernel combination. Experimental results on two well-known benchmarks CIFAR-10 and NUS-WIDE show that the proposed method can achieve 11% and 34% performance gains over state-of-the-art methods.