Semantic boosting cross-modal hashing for efficient multimedia retrieval

Wang, Ke, Tang, Jun, Wang, Nian and Shao, Ling (2016) Semantic boosting cross-modal hashing for efficient multimedia retrieval. Information Sciences, 330. pp. 199-210. ISSN 0020-0255

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Cross-modal hashing aims to embed data from different modalities into a common low-dimensional Hamming space, which serves as an important part in cross-modal retrieval. Although many linear projection methods were proposed to map cross-modal data into a common abstract space, the semantic similarity between cross-modal data was often ignored. To address this issue, we put forward a novel cross-modal hashing method named Semantic Boosting Cross-Modal Hashing (SBCMH). To preserve the semantic similarity, we first apply multi-class logistic regression to project heterogeneous data into a semantic space, respectively. To further narrow the semantic gap between different modalities, we then use a joint boosting framework to learn hash functions, and finally transform the mapped data representations into a measurable binary subspace. Comparative experiments on two public datasets demonstrate the effectiveness of the proposed SBCMH.

Item Type: Article
Uncontrolled Keywords: cross-modal hashing, multimedia retrieval, boosting
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Ay Okpokam
Date Deposited: 23 Dec 2015 10:04
Last Modified: 12 Oct 2019 23:10

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