Learning Effective Binary Descriptors via Cross Entropy

Abstract

Binary descriptors not only are beneficial for similarity search, they are also capable of serving as discriminant features for classification purpose. In this paper we propose a new algorithm based on cross entropy to learn effective binary descriptors, dubbed CE-Bits, providing an alternative to L-2 and hinge loss learning. Because of the usage of cross entropy, a min-max binary NP-hard problem is raised to optimize the binary code during training. We provide a novel solution by breaking the binary code into independent blocks and optimize them individually. Although sub-optimal, our method converges very fast and outperforms its L-2 and hinge loss counterparts. By conducting extensive experiments on several benchmark datasets, we show that CE-Bits efficiently generates effective binary descriptors for both classification and retrieval tasks and outper-forms state-of-the-art supervised hashing algorithms.