Explore instance similarity: An instance correlation based hashing method for multi-label cross-model retrieval |
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| Institution: | 1. Computer Application Research Center, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China;2. College of Computer Science and Technology, Harbin Engineering University, Harbin 155100, China;3. Shenzhen Cyberspace Laboratory, Shenzhen 518055, China;1. Computer Network Information Center, Chinese Academy of Sciences, Beijing, China;2. University of Chinese Academy of Sciences, Beijing, China;3. Institute of Software, Chinese Academy of Sciences, Beijing, China;4. State Grid Energy Research Institute, Beijing, China;1. Sorbonne Université, CNRS, LIP6, Paris F-75005, France;2. Université Paris-Saclay, CNRS, ISP, ENS Paris-Saclay, Cachan, France |
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| Abstract: | With the rapid growth of multimedia data, cross-media hashing has gained more and more attention. However, most existing cross-modal hashing methods ignore the multi-label correlation and only apply binary similarity to measure the correlation between two instances. Most existing methods perform poorly in capturing the relevance between retrieval results and queries since binary similarity measurement has limited abilities to discriminate minor differences among different instances. In order to overcome the mentioned shortcoming, we introduce a novel notion of instance similarity method, which is used to evaluate the semantic correlation between two specific instances in training data. Base on the instance similarity, we also propose a novel deep instance hashing network, which utilizes instance similarity and binary similarity simultaneously for multi-label cross-model retrieval. The experiment results on two real datasets show the superiority of our proposed method, compared with a series of state-of-the-art cross-modal hashing methods in terms of several metric evaluations. |
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