Supervised discrete hashing

Shen, Fumin, Shen, Chunhua, Liu, Wei and Shen, Heng Tao (2015). Supervised discrete hashing. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, United States, (37-45). 7-12 June 2015. doi:10.1109/CVPR.2015.7298598


Author Shen, Fumin
Shen, Chunhua
Liu, Wei
Shen, Heng Tao
Title of paper Supervised discrete hashing
Conference name IEEE Conference on Computer Vision and Pattern Recognition
Conference location Boston, MA, United States
Conference dates 7-12 June 2015
Convener IEEE
Proceedings title 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)   Check publisher's open access policy
Journal name Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition   Check publisher's open access policy
Series Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2015
Sub-type Fully published paper
DOI 10.1109/CVPR.2015.7298598
Open Access Status Not Open Access
ISBN 9781467369640
ISSN 1063-6919
Volume 07-12-June-2015
Start page 37
End page 45
Total pages 9
Language eng
Abstract/Summary Recently, learning based hashing techniques have attracted broad research interests because they can support efficient storage and retrieval for high-dimensional data such as images, videos, documents, etc. However, a major difficulty of learning to hash lies in handling the discrete constraints imposed on the pursued hash codes, which typically makes hash optimizations very challenging (NP-hard in general). In this work, we propose a new supervised hashing framework, where the learning objective is to generate the optimal binary hash codes for linear classification. By introducing an auxiliary variable, we reformulate the objective such that it can be solved substantially efficiently by employing a regularization algorithm. One of the key steps in this algorithm is to solve a regularization sub-problem associated with the NP-hard binary optimization. We show that the sub-problem admits an analytical solution via cyclic coordinate descent. As such, a high-quality discrete solution can eventually be obtained in an efficient computing manner, therefore enabling to tackle massive datasets. We evaluate the proposed approach, dubbed Supervised Discrete Hashing (SDH), on four large image datasets and demonstrate its superiority to the state-of-the-art hashing methods in large-scale image retrieval.
Formatted Abstract/Summary
Recently, learning based hashing techniques have attracted broad research interests due to the resulting efficient storage and retrieval of images, videos, documents, etc. However, a major difficulty of learning to hash lies in handling the discrete constraints imposed on the needed hash codes, which typically makes hash optimizations very challenging (NP-hard in general). In this work, we propose a new supervised hashing framework, where the learning objective for hashing is to make the optimal binary hash codes for classification. By introducing an auxiliary variable, we reformulate the objective such that it can be solved substantially efficiently by using a regularization algorithm. One of the key steps in the algorithm is to solve the regularization sub-problem associated with the NP-hard binary optimization. We show that with cyclic coordinate descent, the sub-problem admits an analytical solution. As such, a high-quality discrete solution can eventually be obtained in an efficient computing manner, which enables to tackle massive datasets. We evaluate the proposed approach, dubbed Supervised Discrete Hashing (SDH), on four large image datasets, and demonstrate that SDH outperforms the state-of-the-art hashing methods in large-scale image retrieval.
Subjects 1712 Software
1707 Computer Vision and Pattern Recognition
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Conference Paper
Sub-type: Fully published paper
Collections: Official 2016 Collection
School of Information Technology and Electrical Engineering Publications
 
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Created: Tue, 10 Mar 2015, 01:31:19 EST by Dr Heng Tao Shen on behalf of School of Information Technol and Elec Engineering