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
November 2021
URL

Abstract

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.

Type
Conference paper
Publication
Proc.~ACM International Conference on Multimedia Retrieval (ICMR)
Bowen Wang
Bowen Wang
Specially-Appointed Researcher/Fellow
Liangzhi Li
Liangzhi Li
Specially-Appointed Assistant Professor

His research interests lie in deep learning, computer vision, robotics, and medical images.

Yuta Nakashima
Yuta Nakashima
Associate Professor

Yuta Nakashima is an associate professor with Institute for Datability Science, Osaka University. His research interests include computer vision, pattern recognition, natural langauge processing, and their applications.

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