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Reinforced Short-Length Hashing.

Authors :
Liu, Xingbo
Nie, Xiushan
Dai, Qi
Huang, Yupan
Lian, Li
Yin, Yilong
Source :
IEEE Transactions on Circuits & Systems for Video Technology. Sep2021, Vol. 31 Issue 9, p3655-3668. 14p.
Publication Year :
2021

Abstract

Given that retrieval and storage have compelling efficiency, similarity-preserving hashing has been extensively employed to approximate nearest neighbor search in large-scale image retrieval. Hash codes that are extremely compact not only can further lower the storage cost, but also accelerate the retrieval speed. However, existing methods perform poorly in retrieval based on an extremely short-length hash code, which attributes to the weak ability of classification and poor distribution of hash bit. To tackle this issue, in this study, we propose a novel reinforced short-length hashing (RSLH). In particular, this proposed method applies the mutual reconstruction between the hash representation and semantic label to retain 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. Besides, we integrate a parameter boosting strategy to strengthen the precision with the consideration of bit balance and uncorrelation constraints. Extensive experiments on three large-scale image benchmarks demonstrate the superior performance of RSLH under various short-length hashing scenarios. [ABSTRACT FROM AUTHOR]

Subjects

Subjects :
*MNEMONICS
*IMAGE retrieval

Details

Language :
English
ISSN :
10518215
Volume :
31
Issue :
9
Database :
Academic Search Index
Journal :
IEEE Transactions on Circuits & Systems for Video Technology
Publication Type :
Academic Journal
Accession number :
153376829
Full Text :
https://doi.org/10.1109/TCSVT.2020.3040863