Person re-identification by semi-supervised dictionary rectification learning

Zongyuan Ding; Hongyuan Wang; Fuhua Chen; Suolan Liu; Tongguang Ni

doi:10.1117/12.2309840

14 May 2018 Person re-identification by semi-supervised dictionary rectification learning

Zongyuan Ding, Hongyuan Wang, Fuhua Chen, Suolan Liu, Tongguang Ni

Proceedings Volume 10670, Real-Time Image and Video Processing 2018; 106700M (2018) https://doi.org/10.1117/12.2309840
Event: SPIE Commercial + Scientific Sensing and Imaging, 2018, Orlando, FL, United States

Abstract

At present, in the field of person re-identification, the commonly used supervised learning algorithms require a large size of labelled sample, which is not conducive to the model promotion. On the other hand, the accuracy of unsupervised learning algorithms is lower than supervised algorithms due to the lack of discriminant information. To address these issues in this paper, we make use of a small size of labelled sample to add discriminant information in the basic dictionary learning. Moreover, the sparse coefficients of dictionary learning are decomposed into a projection problem of the original features, and the projection matrix is trained by labelled samples, which is transformed into a metric learning problem. It thus integrates the advantages of the two methods through combining dictionary learning and metric learning. After the data is trained, a new projection matrix is used to project the unlabeled features into a new feature subspace and the labels of the samples are reconstructed. The semi-supervised learning problem is then transformed to a supervised learning problem with a Laplace term. Experiments on different public pedestrian datasets, such as VIPeR, PRID, iLIDS and CUHK01, show that the recognition accuracy of our method is better than some other existing person reidentification methods.

Citation Download Citation

Zongyuan Ding, Hongyuan Wang, Fuhua Chen, Suolan Liu, and Tongguang Ni "Person re-identification by semi-supervised dictionary rectification learning", Proc. SPIE 10670, Real-Time Image and Video Processing 2018, 106700M (14 May 2018); https://doi.org/10.1117/12.2309840

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Associative arrays

Machine learning

Intelligence systems

Data modeling

RGB color model

Cameras

Evolutionary algorithms

Show All Keywords

Keywords/Phrases

Search In:

Publication Years