Binary constrained deep hashing network for image retrieval without manual annotation

Thanh-Toan Do, Tuan Hoang, Dang-Khoa Le Tan, Trung Pham, Huu Le, Ngai-Man Cheung, Ian Reid

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

6 Citations (Scopus)

Abstract

Learning compact binary codes for image retrieval task using deep neural networks has attracted increasing attention recently. However, training deep hashing networks for the task is challenging due to the binary constraints on the hash codes, the similarity presef1ing property, and the re-quirement for a vast amount of labelled images. To the best of our knowledge, none of the existing methods has tackled all of these challenges completely in a unified framework. In this work, we propose a novel end-to-end deep learning approach for the task, in which the network is trained to produce binary codes directly from image pixels without the need of manual annotation. In particular, to deal with the non-smoothness of binary constraints, we propose a novel painvise constrained loss junction, which simultaneously encodes the distances between pairs of hash codes, and the binary quantization error. In order to train the network with the proposed loss junction, we propose an efficient parameter learning algorithm. In addition, to provide similar / dissimilar training images to train the network, we exploit 3D models reconstructed from unlabelled images for automatic generation of enormous training image pairs. The extensive experiments on image retrieval benchmark datasets demonstrate the improvements of the proposed method over the state-of-the-art compact representation methods on the image retrieval problem.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE Winter Conference on Applications of Computer Vision WACV 2018
EditorsMichael Brown, Yanxi Liu , Peyman Milanfar
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages695-704
Number of pages10
ISBN (Electronic)9781728119755
ISBN (Print)9781728119762
DOIs
Publication statusPublished - 2019
Externally publishedYes
EventIEEE Winter Conference on Applications of Computer Vision 2019 - Waikoloa Village, United States of America
Duration: 7 Jan 201911 Jan 2019
Conference number: 19th
https://wacv19.wacv.net/ (Website)
https://ieeexplore.ieee.org/xpl/conhome/8642793/proceeding (Proceedings)

Publication series

NameProceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
PublisherThe Institute of Electrical and Electronics Engineers, Inc.
ISSN (Print)1550-5790

Conference

ConferenceIEEE Winter Conference on Applications of Computer Vision 2019
Abbreviated titleWACV 2019
CountryUnited States of America
CityWaikoloa Village
Period7/01/1911/01/19
Internet address

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