Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015

Rank Constraints on Joint Dictionary Learning for Image Recognition

Authors
Haohao Meng, Yufeng Chen
Corresponding Author
Haohao Meng
Available Online December 2015.
DOI
https://doi.org/10.2991/icmmcce-15.2015.538How to use a DOI?
Keywords
Rank constraints, Joint dictionary learning, Image recognition.
Abstract
Sparse representation has been extensively applied to image recognition, while learning appropriate dictionaries for image content representation plays a critical role in it. An approach to simultaneously learn a common dictionary and multiple class-specific particular dictionaries achieves state-of-the-art performance. However, how to separate the particularity and commonality correctly is a quite important problem. Meanwhile, there exists an over-fitting phenomenon in the dictionary learned from given training samples which lie on a low dimensional subspace in that data samples can be linearly represented by dictionary. In this paper, we propose rank constraints on the joint dictionary learning (RC-JDL) algorithm to solve the above questions. Extensive experimental results on public available databases demonstrate the effectiveness of the proposed algorithm.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Cite this article

TY  - CONF
AU  - Haohao Meng
AU  - Yufeng Chen
PY  - 2015/12
DA  - 2015/12
TI  - Rank Constraints on Joint Dictionary Learning for Image Recognition
BT  - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmcce-15.2015.538
DO  - https://doi.org/10.2991/icmmcce-15.2015.538
ID  - Meng2015/12
ER  -