Deep Multi-Region Hashing
Quan Zhou, Xiushan Nie, Yang Shi, Xingbo Liu, Yilong Yin
Abstract
Hashing has been widely used for large-scale approximate nearest neighbors retrieval own to its high efficiency. In the existing hashing methods, deep supervised hashing methods have achieved the best performance by utilizing the semantic labels on data with deep learning. However, most of these methods only consider the semantics of whole image but ignore the local information which contains much more semantic details. Evidently, the semantic details are beneficial for hash learning. To address this issue, in this paper, we proposed a novel Deep Multi-Region Hashing (DMRH) method to fully utilize the semantic details, which uses overlapping N × N regions of an image to learn N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> hash codes for getting a final hash code. Extensive experimental results with three datasets show that DMRH can achieve state-of-the-art performance.
BibTeX
@inproceedings{icassp2020_deepmultiregionh,
title = {Deep Multi-Region Hashing},
author = {Quan Zhou and Xiushan Nie and Yang Shi and Xingbo Liu and Yilong Yin},
booktitle = {ICASSP 2020},
year = {2020}
}