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2018 | OriginalPaper | Chapter

DDSH: Deep Distribution-Separating Hashing for Image Retrieval

Authors : Junjie Chen, Anran Wang

Published in: Advances in Multimedia Information Processing – PCM 2017

Publisher: Springer International Publishing

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Abstract

With the rapid growth of web images, binary hashing method has received increasing attention due to the storage efficiency and the ability for fast retrieval. Recently, deep hashing methods have achieved the state-of-the-art performance by utilizing deep neural networks in hash code learning. Most of these methods are trained with the supervision of triplet labels or pairwise relationships. In this paper, we propose a deep hashing framework called deep distribution-separating hashing (DDSH) method. The main novelty of our learning framework lies in the supervision which enforces to separate the distribution of similar pairs from the distribution of dissimilar pairs. In this way, the gap between similar pairs and dissimilar pairs is enlarged. Experimental results show that our proposed deep hashing method outperforms state-of-the-art approaches on two widely used benchmark datasets: CIFAR-10 and PASCAL VOC 2007.

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Metadata
Title
DDSH: Deep Distribution-Separating Hashing for Image Retrieval
Authors
Junjie Chen
Anran Wang
Copyright Year
2018
Book
Advances in Multimedia Information Processing – PCM 2017

Print ISBN: 978-3-319-77382-7

Electronic ISBN: 978-3-319-77383-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-77383-4_55