2020/04/24 by Xingbo Liu, Liu, Xingbo, Xiushan Nie +7
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multimedia (cs.MM) #Robotics and Sensor-Based Localization
paper · pdf · doi:10.48550/arxiv.2004.11511
openalex publication_date 2020/04/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Due to the compelling efficiency in retrieval and storage, similarity-preserving hashing has been widely applied to approximate nearest neighbor search in large-scale image retrieval. However, existing methods have poor performance in retrieval using an extremely short-length hash code due to weak ability of classification and poor distribution of hash bit. To address this issue, in this study, we propose a novel reinforcing short-length hashing (RSLH). In this proposed RSLH, mutual reconstruction between the hash representation and semantic labels is performed to preserve the semantic information. Furthermore, to enhance the accuracy of hash representation, a pairwise similarity matrix is designed to make a balance between accuracy and training expenditure on memory. In addition, a parameter boosting strategy is integrated to reinforce the precision with hash bits fusion. Extensive experiments on three large-scale image benchmarks demonstrate the superior performance of RSLH under various short-length hashing scenarios.