Large-scale content-based image retrieval system with metric learning and discrete binary encoding

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Abstract

Hashing methods have been widely used in large-scale content-based image retrieval systems, which have extensive and practical applications in search engines and social networks. However, existing deep supervised hashing methods focus on enforcing the supervision information to learn the discrete hash codes, ignoring the latent clustering in the Hamming space. In addition, the discrete constraints in the above optimization problem are usually hard to solve. To address these issues, we propose a new deep supervised hashing method for image retrieval tasks. Specifically, we utilized a metric learning strategy to minimize the intra-class Hamming distance and maximize the inter-class Hamming distance as much as possible. Then, a novel label fitting approach was developed to generate discrete proxies and hash codes simultaneously. Finally, an effective alternating optimization method was designed to solve the discrete optimization problem. Experimental results on various datasets demonstrate that the proposed method can achieve promising retrieval performance.

Keywords

image retrieval / metric learning / deep learning / discrete binary encoding

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