Semantic Hierarchy Preserving Deep Hashing For Large-scale Image Retrieval
Ming Zhang, Xuefei Zhe, Le Ou-Yang, Shifeng Chen, Hong Yan . Arxiv 2019 – 3 citations
[Paper]
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.