As an important element of social media, social images become more and more important to our daily life. Recently, smart hashing scheme has been emerging as a promising approach to support fast social image search. Leveraging semantic labels have shown effectiveness for hashing. However, semantic labels tend to be limited in terms of quantity and quality. In this paper, we propose Multi-Task Multi-modal Semantic Hashing (MTMSH) to index large scale social image data collection with limited supervision. MTMSH improves search accuracy via improving more semantic information from two aspects. First, latent multi-modal structure among labeled and unlabeled data, is explored by Multiple Anchor Graph Learning (MAGL) to enhance the quantity of semantic information. In addition, multi-task based Share Hash Space Learning (SHSL) is proposed to improve the semantic quality. Further, MGAL and SHSL are integrated using a joint framework, where semantic function and hash functions mutually reinforce each other. Then, an alternating algorithm, whose time complexity is linear to the size of training data, is also proposed. Experimental results on two large scale real world image datasets demonstrate the effectiveness and efficiency of MTMSH.
['Liang Xie', 'Lei Zhu', 'Zhiyong Cheng']
Multi-Task Multi-modal Semantic Hashing for Web Image Retrieval with Limited Supervision