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2016 | OriginalPaper | Buchkapitel

Deep Multi-level Hashing Codes for Image Retrieval

verfasst von : Zhenjiang Dong, Ge Song, Xia Jia, Xiaoyang Tan

Erschienen in: Intelligent Visual Surveillance

Verlag: Springer Singapore

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Abstract

In this paper, we propose a deep siamese convolutional neutral network (DSCNN) to learn semantic-preserved global-level and local-level hashing codes simultaneously for effective image retrieval. Particularly, we analyze the visual attention characteristic inside hash bits by activation map of deep convolutional feature and propose a novel approach of bit selecting to reinforce the pertinence of local-level code. Finally, unlike most existing retrieval methods which use global or unsupervised local descriptors separately, leading to unexpected precision, we present a multi-level hash search method, taking advantage of both local and global properties of deep features. The experimental results show that our method outperforms several state-of-the-art on the Oxford 5k/105k and Paris 6k datasets.

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Vorheriges Kapitel Person Re-identification by Multiple Feature Representations and Metric Learning

Nächstes Kapitel Salient Object Detection from Single Haze Images via Dark Channel Prior and Region Covariance Descriptor

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Titel: Deep Multi-level Hashing Codes for Image Retrieval
verfasst von: Zhenjiang Dong
Ge Song
Xia Jia
Xiaoyang Tan
Verlag: Springer Singapore
Buch: Intelligent Visual Surveillance
Print ISBN: 978-981-10-3475-6

Electronic ISBN: 978-981-10-3476-3

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-981-10-3476-3_11

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