research-article

Semi-Supervised Metric Learning-Based Anchor Graph Hashing for Large-Scale Image Retrieval

Authors:

Zhen YangAuthors Info & Claims

IEEE Transactions on Image Processing, Volume 28, Issue 2

Pages 739 - 754

https://doi.org/10.1109/TIP.2018.2860898

Published: 01 February 2019 Publication History

Abstract

Hashing-based image retrieval methods have become a cutting-edge topic in the information retrieval domain due to their high efficiency and low cost. In order to perform efficient hash learning by simultaneously preserving the semantic similarity and data structures in the feature space, this paper presents the semi-supervised metric learning-based anchor graph hashing method. Our proposed approach can be divided into three parts. First, we exploit a transformation matrix to construct the anchor-based similarity graph of the training set. Second, we propose the objective function based on the triplet relationship, in which the optimal transformation matrix can be learned by using the smoothness of labels and the margin hinge loss incurred by the triplet constraint. Moreover, the stochastic gradient descent (SGD) method leverages the gradient on each triplet to update the transformation matrix. Finally, a penalty factor is designed to accelerate the execution speed of SGD. Through comparison with the retrieval results of several state-of-the-art methods on several image benchmarks, the experiments validate the feasibility and advantages of our proposed methods.

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Deng ZLai ZDing YKong HWu XGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Deep Scaling Factor Quantization Network for Large-scale Image RetrievalProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658017(851-859)Online publication date: 30-May-2024
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He ZQin JFei LZhao SWen JWang B(2023)Sparse Graph Hashing with Spectral RegressionAdvances in Computer Graphics10.1007/978-3-031-50078-7_4(41-53)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-50078-7_4
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cover image IEEE Transactions on Image Processing

IEEE Transactions on Image Processing Volume 28, Issue 2

Feb. 2019

511 pages

ISSN:1057-7149

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1057-7149 © 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

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Cited By

Deng ZLai ZDing YKong HWu XGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Deep Scaling Factor Quantization Network for Large-scale Image RetrievalProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658017(851-859)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658017
Liu CNie FWang RLi X(2023)Scalable Fuzzy Clustering With Anchor GraphIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.320068535:8(8503-8514)Online publication date: 1-Aug-2023
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He ZQin JFei LZhao SWen JWang B(2023)Sparse Graph Hashing with Spectral RegressionAdvances in Computer Graphics10.1007/978-3-031-50078-7_4(41-53)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-50078-7_4
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Li ZTang JZhang LYang J(2020)Weakly-supervised Semantic Guided Hashing for Social Image RetrievalInternational Journal of Computer Vision10.1007/s11263-020-01331-0128:8-9(2265-2278)Online publication date: 1-Sep-2020
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