Supervised Ranking Hash for Semantic Similarity Search

The era of big data has spawned unprecedented interests in developing hashing algorithms for their storage efficiency and effectiveness in fast nearest neighbor search in large-scale databases. Most of the existing hash learning algorithms focus on learning hash functions which generate binary codes by numeric quantization of some projected feature space. In this work, we propose a novel hash learning framework that encodes features' ranking orders instead of quantizing their numeric values in a number of optimal low-dimensional ranking subspaces. We formulate the ranking-based hash learning problem as the optimization of a continuous probabilistic error function using softmax approximation and present an efficient learning algorithm to solve the problem. Our work is a generalization of the Winner-Take-All (WTA) hashing algorithm and naturally enjoys the numeric stability benefits of rank correlation measures while being optimized to achieve high precision at extremely short code length. We extensively evaluate the proposed algorithm in several datasets and demonstrate superior performance against several state-of-the-arts.