Discrete Hashing Based on Point-Wise Supervision and Inner Product

Recent years has witnessed an increase popularity of supervised hashing in vision problems like image retrieval. Compared with unsupervised hashing, supervised hashing accuracy can be boosted by leveraging semantic information. However, the existing supervised methods either lack of adequate performance or often incur a low quality optimization process by dropping the discrete constraints. In this work, we propose a novel supervised hashing framework called discrete hashing based on point-wise supervision and inner product (PSIPDH) which using point-wise supervised information make hash code effectively correspond to the semantic information, on the basis of which the coded inner product is manipulated to introduce the punishment of Hamming distance. By introducing two kinds of supervisory information, a discrete solution can be applied that code generation and hash function learning processes are seen as separate steps and discrete hashing code can be directly learned from semantic labels bit by bit. Experiment results on data sets with semantic labels can demonstrate the superiority of PSIPDH to the state-of-the-art hashing methods.