کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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392848 | 665182 | 2016 | 12 صفحه PDF | دانلود رایگان |
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.
Journal: Information Sciences - Volume 330, 10 February 2016, Pages 199–210