Semantic Hierarchy Preserving Deep Hashing For Large-scale Image Retrieval
Zhang Ming, Zhe Xuefei, Ou-yang Le, Chen Shifeng, Yan Hong. Arxiv 2019
Deep hashing models have been proposed as an efficient method for large-scale similarity search. However, 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.
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