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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Veröffentlicht in:Proceedings - International Conference on Parallel and Distributed Systems S. 1437 - 1444
Hauptverfasser: Cao, Yuan, Meng, Fanlei, Wu, Xiangyu, Wang, Zijie
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 17.12.2023
Schlagworte:
Benchmark testing
Binary codes
generative model
hashing
Image retrieval
Scalability
Semantics
Training
Training data
unsupervised learning
ISSN:2690-5965
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Zusammenfassung: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.
ISSN:2690-5965
DOI:10.1109/ICPADS60453.2023.00204