Unsupervised Multiview Distributed Hashing for Large-Scale Retrieval

Multi-view hashing (MvH) learns compact hash code by efficiently integrating multi-view data, and has achieved promising performance in large-scale retrieval task. In real-world applications, multi-view data is often stored or collected in different locations, and learning hash code in such case is...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2022-12, Vol.32 (12), p.8837-8848
Main Authors: Shen, Xiaobo, Tang, Yunpeng, Zheng, Yuhui, Yuan, Yun-Hao, Sun, Quan-Sen
Format: Article
Language:English
Subjects:
Clustering
Codes
Computer aided instruction
Consistency
Costs
Datasets
Distance learning
Distributed databases
distributed learning
Hash functions
Hashing
Learning
multi-view data
Nodes
Retrieval
Training
Training data
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Summary:Multi-view hashing (MvH) learns compact hash code by efficiently integrating multi-view data, and has achieved promising performance in large-scale retrieval task. In real-world applications, multi-view data is often stored or collected in different locations, and learning hash code in such case is more challenging yet less studied. In addition, unsupervised MvHs hardly achieve impressive retrieval performance due to absence of supervision. To fulfill this gap, this paper introduces a novel unsupervised multi-view distributed hashing (UMvDisH) to learn hash code from multi-view data, which is distributed in different nodes of a network. UMvDisH jointly performs latent factor model and spectral clustering to generate latent hash code and pseudo label respectively in each node. The consistency between hash code and pseudo label improves discrimination of hash code. The proposed distributed learning problem is divided into a set of decentralized subproblems by imposing local consistency among neighbor nodes. As such, the subproblems can be solved in parallel, and training time can be reduced. The communication cost is low due to no exchange of training data. Experimental results on four benchmark image datasets including a very large-scale image dataset show that UMvDisH achieves comparable retrieval performance and trains faster than state-of-the-art unsupervised MvHs in the distributed setting.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2022.3197849