Fast training of triplet-based deep binary embedding networks

Bohan Zhuang, Guosheng Lin, Chunhua Shen, Ian Reid

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

77 Citations (Scopus)

Abstract

In this paper, we aim to learn a mapping (or embedding) from images to a compact binary space in which Hamming distances correspond to a ranking measure for the image retrieval task. We make use of a triplet loss because this has been shown to be most effective for ranking problems. However, training in previous works can be prohibitively expensive due to the fact that optimization is directly performed on the triplet space, where the number of possible triplets for training is cubic in the number of training examples. To address this issue, we propose to formulate high-order binary codes learning as a multi-label classification problem by explicitly separating learning into two interleaved stages. To solve the first stage, we design a large-scale high-order binary codes inference algorithm to reduce the high-order objective to a standard binary quadratic problem such that graph cuts can be used to efficiently infer the binary codes which serve as the labels of each training datum. In the second stage we propose to map the original image to compact binary codes via carefully designed deep convolutional neural networks (CNNs) and the hashing function fitting can be solved by training binary CNN classifiers. An incremental/interleaved optimization strategy is proffered to ensure that these two steps are interactive with each other during training for better accuracy. We conduct experiments on several benchmark datasets, which demonstrate both improved training time (by as much as two orders of magnitude) as well as producing state-of-the-art hashing for various retrieval tasks.

Original languageEnglish
Title of host publicationProceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
EditorsLourdes Agapito, Tamara Berg, Jana Kosecka, Lihi Zelnik-Manor
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages5955-5964
Number of pages10
ISBN (Electronic)9781467388504
ISBN (Print)9781467388528
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventIEEE Conference on Computer Vision and Pattern Recognition 2016 - Las Vegas, United States of America
Duration: 27 Jun 201630 Jun 2016
http://cvpr2016.thecvf.com/
https://ieeexplore.ieee.org/xpl/conhome/7776647/proceeding (Proceedings)

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2016
Abbreviated titleCVPR 2016
CountryUnited States of America
CityLas Vegas
Period27/06/1630/06/16
Internet address

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