Semantic Boosting Cross-Modal Hashing for efficient multimedia retrieval

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• 作者：Ke Wang^a ; ^b ; ¹ ; ^{wangke4747@126.com" class="auth_mail" title="E-mail the corresponding author} ; Jun Tang^a ; ^b ; ¹ ; ^{tangjunahu@163.com" class="auth_mail" title="E-mail the corresponding author} ; Nian Wang^a ; ^{wangnianahu@163.com" class="auth_mail" title="E-mail the corresponding author} ; Ling Shao^c ; ^d ; ^{ling.shao@ieee.org" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Cross-modal hashing ; Multimedia retrieval ; Boosting
• 刊名：Information Sciences
• 出版年：2016
• 出版时间：10 February 2016
• 年：2016
• 卷：330
• 期：Complete
• 页码：199-210
• 全文大小：3596 K

文摘

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.