Rank Constraints on Joint Dictionary Learning for Image Recognition
Haohao Meng, Yufeng Chen
Available Online December 2015.
- https://doi.org/10.2991/icmmcce-15.2015.538How to use a DOI?
- Rank constraints, Joint dictionary learning, Image recognition.
- 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.
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 -