Discrete hashing with triple supervision learning |
| |
Affiliation: | 1. Department of Electrical Engineering, Indian Institute of Technology Patna, Bihta 801 106, Patna, India;2. Department of Physics, Indian Institute of Technology Patna, Bihta 801 106, Patna, India;1. Engineering Research Center of Wideband Wireless Communication Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;2. Hangzhou Joyware Limited Corporation, Hangzhou 310051, China |
| |
Abstract: | In recent years, discrete supervised hashing methods have attracted increasing attention because of their high retrieval efficiency and precision. However, in these methods, some effective semantic information is typically neglected, which means that all the information is not sufficiently utilized. Moreover, these methods often only decompose the first-order features of the original data, ignoring the more fine-grained higher-order features. To address these problems, we propose a supervised hashing learning method called discrete hashing with triple supervision learning (DHTSL). Specifically, we integrate three aspects of semantic information into this method: (1) the bidirectional mapping of semantic labels; (2) pairwise similarity relations; (3) second-order features from the original data. We also design a discrete optimization method to solve the proposed objective function. Moreover, an out-of-sample extension strategy that can better maintain the independence and balance of hash codes is employed to improve retrieval performance. Extensive experiments on three widely used datasets demonstrate its superior performance. |
| |
Keywords: | ANN search Discrete hashing Triple supervision |
本文献已被 ScienceDirect 等数据库收录! |
|