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Deep Binary Representation for Efficient Image Retrieval
Joint Authors
Yang, Xiaokang
Zhang, Wenjun
Lu, Xuchao
Song, Li
Xie, Rong
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-11-12
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
With the fast growing number of images uploaded every day, efficient content-based image retrieval becomes important.
Hashing method, which means representing images in binary codes and using Hamming distance to judge similarity, is widely accepted for its advantage in storage and searching speed.
A good binary representation method for images is the determining factor of image retrieval.
In this paper, we propose a new deep hashing method for efficient image retrieval.
We propose an algorithm to calculate the target hash code which indicates the relationship between images of different contents.
Then the target hash code is fed to the deep network for training.
Two variants of deep network, DBR and DBR-v3, are proposed for different size and scale of image database.
After training, our deep network can produce hash codes with large Hamming distance for images of different contents.
Experiments on standard image retrieval benchmarks show that our method outperforms other state-of-the-art methods including unsupervised, supervised, and deep hashing methods.
American Psychological Association (APA)
Lu, Xuchao& Song, Li& Xie, Rong& Yang, Xiaokang& Zhang, Wenjun. 2017. Deep Binary Representation for Efficient Image Retrieval. Advances in Multimedia،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1122386
Modern Language Association (MLA)
Lu, Xuchao…[et al.]. Deep Binary Representation for Efficient Image Retrieval. Advances in Multimedia No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1122386
American Medical Association (AMA)
Lu, Xuchao& Song, Li& Xie, Rong& Yang, Xiaokang& Zhang, Wenjun. Deep Binary Representation for Efficient Image Retrieval. Advances in Multimedia. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1122386
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1122386