Semantic Hierarchy Preserving Deep Hashing for Large-Scale Image Retrieval

Ming Zhang, Xuefei Zhe, Le Ou-Yang, Shifeng Chen, Hong Yan

Abstract: Deep hashing models have been proposed as an efficient method for large-scale similarity search. How-ever, most existing deep hashing methods only utilize fine-level labels for training while ignoring the natural semantic hierarchy structure. This paper presents an effective method that preserves the classwise similarity of full-level semantic hierarchy for large-scale image retrieval. Experiments on two benchmark datasets show that our method helps improve the fine-level retrieval performance. Moreover, with the help of the semantic hierarchy, it can produce significantly better binary codes for hierarchical retrieval, which indicates its potential of providing more user-desired retrieval results. The codes are available at https://github.com/mzhang367/hpdh.git.