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arXiv · 1901.11259

Semantic Hierarchy Preserving Deep Hashing for Large-scale Image Retrieval

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

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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Ming Zhang, Xuefei Zhe, Le Ou-Yang, Shifeng Chen, Hong Yan. 2021-06-22. Semantic Hierarchy Preserving Deep Hashing for Large-scale Image Retrieval. https://arxiv.org/abs/1901.11259

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