Image Retrieval by Hierarchy-aware Deep Hashing Based on Multi-task Learning
Bowen Wang, Liangzhi Li, Yuta Nakashima, Takehiro Yamamoto, Hiroaki Ohshima, Yoshiyuki Shoji, Kenro Aihara, Noriko Kando · 2021
Deep hashing has been widely used to approximate nearest-neighbor search for image retrieval tasks. Most of them are trained with image-label pairs without any inter-label relationship, which may not make full use of the real-world data. This paper presents deep hashing, named HA2SH, that leverages multiple types of labels with hierarchical structures that an ethnological museum assigns to their artifacts. We experimentally prove that HA2SH can learn to generate hashes that give a better retrieval performance. Our code is available at https://github.com/wbw520/minpaku.