Reinforced Short-Length Hashing

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 r...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2021-09, Vol.31 (9), p.1-1
Main Authors: Liu, Xingbo, Nie, Xiushan, Dai, Qi, Huang, Yupan, Lian, Li, Yin, Yilong
Format: Article
Language:English
Subjects:
ANN search
Boosting
Discrete hashing
Hash functions
Image management
Image retrieval
Model boosting
Optimization
Representations
Semantics
Short-length hashing
Similarity
Synchronous digital hierarchy
Training
Training data
Two-step hashing
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Summary: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.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2020.3040863