A Survey on Deep Hashing Methods

• URL: http://arxiv.org/abs/2003.03369v5
• Date: Sat, 23 Apr 2022 11:43:07 GMT
• Title: A Survey on Deep Hashing Methods
• Authors: Xiao Luo, Haixin Wang, Daqing Wu, Chong Chen, Minghua Deng, Jianqiang Huang, Xian-Sheng Hua
• Abstract summary: Nearest neighbor search aims to obtain the samples in the database with the smallest distances from them to the queries. With the development of deep learning, deep hashing methods show more advantages than traditional methods. Deep supervised hashing is categorized into pairwise methods, ranking-based methods, pointwise methods and quantization. Deep unsupervised hashing is categorized into similarity reconstruction-based methods, pseudo-label-based methods and prediction-free self-supervised learning-based methods.
• Score: 52.326472103233854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nearest neighbor search aims to obtain the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining. Hashing is one of the most widely used methods for its computational and storage efficiency. With the development of deep learning, deep hashing methods show more advantages than traditional methods. In this survey, we detailedly investigate current deep hashing algorithms including deep supervised hashing and deep unsupervised hashing. Specifically, we categorize deep supervised hashing methods into pairwise methods, ranking-based methods, pointwise methods as well as quantization according to how measuring the similarities of the learned hash codes. Moreover, deep unsupervised hashing is categorized into similarity reconstruction-based methods, pseudo-label-based methods and prediction-free self-supervised learning-based methods based on their semantic learning manners. We also introduce three related important topics including semi-supervised deep hashing, domain adaption deep hashing and multi-modal deep hashing. Meanwhile, we present some commonly used public datasets and the scheme to measure the performance of deep hashing algorithms. Finally, we discuss some potential research directions in conclusion.

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