MATEC Web Conf.
Volume 173, 20182018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018)
|Number of page(s)||6|
|Section||Digital Signal and Image Processing|
|Published online||19 June 2018|
Low-Rank Optimization Dictionary Training for Image Classification
Chongqing Land Resources Housing Surveying and Planning Institute, 40112, Chongqing, China
2 School of Remote Sensing and Information Engineering, Wuhan University, 430079, Wuhan, China
3 Chongqing industrial& commercial school, 472289, Chongqing, China
* Corresponding author: email@example.com
Bag-of-words model has been extremely popular in image categorization. The method of constructing the dictionary is important. In this paper a category constrained low-rank optimization dictionary training approach is proposed for the dictionary construction. Through the low-rank optimization, the rank of the coefficient matrix constructed by same category images is minimized. Experimental results show that the proposed method can obtain better performance on two standard image databases (Caltech-101 and Caltech-256) than not employing the category constrained low-rank optimization.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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