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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 7: AAAI-22 Technical Tracks 7

Deep Unsupervised Hashing with Latent Semantic Components

February 1, 2023

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Authors

Qinghong Lin

Shenzhen University


Xiaojun Chen

Shenzhen University


Qin Zhang

Shenzhen University


Shaotian Cai

Shenzhen University


Wenzhe Zhao

Tencent Computer System Co., Ltd.


Hongfa Wang

Tencent Computer System Co., Ltd.


DOI:

10.1609/aaai.v36i7.20713


Abstract:

Deep unsupervised hashing has been appreciated in the regime of image retrieval. However, most prior arts failed to detect the semantic components and their relationships behind the images, which makes them lack discriminative power. To make up the defect, we propose a novel Deep Semantic Components Hashing (DSCH), which involves a common sense that an image normally contains a bunch of semantic components with homology and co-occurrence relationships. Based on this prior, DSCH regards the semantic components as latent variables under the Expectation-Maximization framework and designs a two-step iterative algorithm with the objective of maximum likelihood of training data. Firstly, DSCH constructs a semantic component structure by uncovering the fine-grained semantics components of images with a Gaussian Mixture Modal~(GMM), where an image is represented as a mixture of multiple components, and the semantics co-occurrence are exploited. Besides, coarse-grained semantics components, are discovered by considering the homology relationships between fine-grained components, and the hierarchy organization is then constructed. Secondly, DSCH makes the images close to their semantic component centers at both fine-grained and coarse-grained levels, and also makes the images share similar semantic components close to each other. Extensive experiments on three benchmark datasets demonstrate that the proposed hierarchical semantic components indeed facilitate the hashing model to achieve superior performance.

Topics: AAAI

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HOW TO CITE:

Qinghong Lin||Xiaojun Chen||Qin Zhang||Shaotian Cai||Wenzhe Zhao||Hongfa Wang Deep Unsupervised Hashing with Latent Semantic Components Proceedings of the AAAI Conference on Artificial Intelligence (2022) 7488-7496.

Qinghong Lin||Xiaojun Chen||Qin Zhang||Shaotian Cai||Wenzhe Zhao||Hongfa Wang Deep Unsupervised Hashing with Latent Semantic Components AAAI 2022, 7488-7496.

Qinghong Lin||Xiaojun Chen||Qin Zhang||Shaotian Cai||Wenzhe Zhao||Hongfa Wang (2022). Deep Unsupervised Hashing with Latent Semantic Components. Proceedings of the AAAI Conference on Artificial Intelligence, 7488-7496.

Qinghong Lin||Xiaojun Chen||Qin Zhang||Shaotian Cai||Wenzhe Zhao||Hongfa Wang. Deep Unsupervised Hashing with Latent Semantic Components. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.7488-7496.

Qinghong Lin||Xiaojun Chen||Qin Zhang||Shaotian Cai||Wenzhe Zhao||Hongfa Wang. 2022. Deep Unsupervised Hashing with Latent Semantic Components. "Proceedings of the AAAI Conference on Artificial Intelligence". 7488-7496.

Qinghong Lin||Xiaojun Chen||Qin Zhang||Shaotian Cai||Wenzhe Zhao||Hongfa Wang. (2022) "Deep Unsupervised Hashing with Latent Semantic Components", Proceedings of the AAAI Conference on Artificial Intelligence, p.7488-7496

Qinghong Lin||Xiaojun Chen||Qin Zhang||Shaotian Cai||Wenzhe Zhao||Hongfa Wang, "Deep Unsupervised Hashing with Latent Semantic Components", AAAI, p.7488-7496, 2022.

Qinghong Lin||Xiaojun Chen||Qin Zhang||Shaotian Cai||Wenzhe Zhao||Hongfa Wang. "Deep Unsupervised Hashing with Latent Semantic Components". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.7488-7496.

Qinghong Lin||Xiaojun Chen||Qin Zhang||Shaotian Cai||Wenzhe Zhao||Hongfa Wang. "Deep Unsupervised Hashing with Latent Semantic Components". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 7488-7496.

Qinghong Lin||Xiaojun Chen||Qin Zhang||Shaotian Cai||Wenzhe Zhao||Hongfa Wang. Deep Unsupervised Hashing with Latent Semantic Components. AAAI[Internet]. 2022[cited 2023]; 7488-7496.


ISSN: 2374-3468


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