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Deep Cross-modal Proxy Hashing

2022; IEEE Computer Society; Linguagem: Inglês

10.1109/tkde.2022.3187023

2326-3865

Rong-Cheng Tu, Xian-Ling Mao, Rongxin Tu, Binbin Bian, Chengfei Cai, Hongfa wang, Wei Wei, Heyan Huang,

Video Surveillance and Tracking Methods

Due to the high retrieval efficiency and low storage cost for cross-modal search tasks, cross-modal hashing methods have attracted considerable attention from the researchers. For the supervised cross-modal hashing methods, how to make the learned hash codes sufficiently preserve semantic information contained in the label of datapoints is the key to further enhance the retrieval performance. Hence, almost all supervised cross-modal hashing methods usually depend on defining similarities between datapoints with the label information to guide the hashing model learning fully or partly. However, the defined similarity between datapoints can only capture the label information of datapoints partially and misses abundant semantic information, which then hinders the further improvement of retrieval performance. Thus, in this paper, different from previous works, we propose a novel cross-modal hashing method without defining the similarity between datapoints, called Deep Cross-modal Proxy Hashing (DCPH). Specifically, DCPH first trains a proxy hashing network to transform each category information of a dataset into a semantic discriminative hash code, called proxy hash code. Each proxy hash code can preserve the semantic information of its corresponding category well. Next, without defining the similarity between datapoints to supervise the training process of the modality-specific hashing networks, we propose a novel margin-dynamic-softmax loss to directly utilize the proxy hashing codes as supervised information. Finally, by minimizing the novel margin-dynamic-softmax loss , the modality-specific hashing networks can be trained to generate hash codes that can simultaneously preserve the cross-modal similarity and abundant semantic information well. Extensive experiments on three benchmark datasets show that the proposed method outperforms the state-of-the-art baselines in the cross-modal retrieval tasks.