Generative Enhancement-based Similarity Prediction Hashing for Image Retrieval
Hashing is frequently used in approximate nearest neighbor search due to its storage and search efficiency. On account of the bottleneck of traditional learning to hash methods, deep-based learning to hash has gained quite a popularity among researchers recently. While such methods show a promising...
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| Vydáno v: | Proceedings - International Conference on Parallel and Distributed Systems s. 1437 - 1444 |
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| Jazyk: | angličtina |
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IEEE
17.12.2023
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| ISSN: | 2690-5965 |
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| Abstract | Hashing is frequently used in approximate nearest neighbor search due to its storage and search efficiency. On account of the bottleneck of traditional learning to hash methods, deep-based learning to hash has gained quite a popularity among researchers recently. While such methods show a promising performance gain by utilizing deep neural networks into its end-to-end training process to generate compact binary codes, the intrinsic connections between components make it unfeasible to optimize the architecture significantly. Subject to noise interference and absence of similarity labels of training data, normal unsupervised deep models even carry a noticeable deviation at representation learning stage. By integrating Generative Adversarial Network, this paper presents a novel architecture for generating compact hash codes from an extended set derived from original images while using the benefit of similarity prediction. Extensive experiments conducted on three benchmarks, NUS-WIDE, CIFAR-10, and MS-COCO illustrate that our method outperforms state-of-the-art image retrieval models and generates high-quality binary hash codes seamlessly. |
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| AbstractList | Hashing is frequently used in approximate nearest neighbor search due to its storage and search efficiency. On account of the bottleneck of traditional learning to hash methods, deep-based learning to hash has gained quite a popularity among researchers recently. While such methods show a promising performance gain by utilizing deep neural networks into its end-to-end training process to generate compact binary codes, the intrinsic connections between components make it unfeasible to optimize the architecture significantly. Subject to noise interference and absence of similarity labels of training data, normal unsupervised deep models even carry a noticeable deviation at representation learning stage. By integrating Generative Adversarial Network, this paper presents a novel architecture for generating compact hash codes from an extended set derived from original images while using the benefit of similarity prediction. Extensive experiments conducted on three benchmarks, NUS-WIDE, CIFAR-10, and MS-COCO illustrate that our method outperforms state-of-the-art image retrieval models and generates high-quality binary hash codes seamlessly. |
| Author | Wang, Zijie Meng, Fanlei Wu, Xiangyu Cao, Yuan |
| Author_xml | – sequence: 1 givenname: Yuan surname: Cao fullname: Cao, Yuan email: cy8661@ouc.edu.cn organization: Ocean University of China,School of Computer Science and Technology,Qingdao,China – sequence: 2 givenname: Fanlei surname: Meng fullname: Meng, Fanlei organization: Ocean University of China,School of Computer Science and Technology,Qingdao,China – sequence: 3 givenname: Xiangyu surname: Wu fullname: Wu, Xiangyu organization: Ocean University of China,School of Computer Science and Technology,Qingdao,China – sequence: 4 givenname: Zijie surname: Wang fullname: Wang, Zijie organization: Ocean University of China,School of Computer Science and Technology,Qingdao,China |
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| Snippet | Hashing is frequently used in approximate nearest neighbor search due to its storage and search efficiency. On account of the bottleneck of traditional... |
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| SubjectTerms | Benchmark testing Binary codes generative model hashing Image retrieval Scalability Semantics Training Training data unsupervised learning |
| Title | Generative Enhancement-based Similarity Prediction Hashing for Image Retrieval |
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