Semantic boosting cross-modal hashing for efficient multimedia retrieval

Ke Wang, Jun Tang, Nian Wang, Ling Shao

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)199-210
JournalInformation Sciences
Publication statusPublished - 10 Feb 2016


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