Wheat Varieties Identification Research Based on Sparse Representation Method of Dictionary Learning
AUTHORS
Yanping Yang,Department of Basic, Yellow River Conservancy Technical Institute, Kaifeng 475003, China;
Ruiguang Li,College English Department, Henan University, Kaifeng 475001, China;
Lijuan Feng,College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China.
ABSTRACT
With the rapid development of computer vision technology, the use of machine vision to replace artificial is widely used in the product detection and classification. The conventional sparse representation methods needs a large amount of training samples to improve the ability of sparse representation of a dictionary. This results into a large dictionary size and an immense memory requirement, which often leads to low efficiency in actual applications. In this paper, a novel method of identification and classification of the wheat varieties is given based on the sparse representation method with the dictionary learning technique. In the given method, the K-SVD algorithm is utilized to train the feature dictionary, the number of the atoms in which is effectively reduced, compared with the method of identification and classification of the wheat varieties based on the conventional sparse representation method. The final test simulation verifies the effectiveness and feasibility of the new identification and classification method of wheat varieties, and compares it with the conventional the identification and classification method of wheat varieties.
KEYWORDS
image processing; sparse representation; K-SVD; wheat varieties identification
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CITATION
COPYRIGHT
© 2018 Li Ruiguang et al. Published by Global Vision Press. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CCBY4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.