Research Article
Extending Color Properties for Texture Descriptor Based on Local Ternary Patterns to Classify Rice Varieties
@ARTICLE{10.4108/eai.7-3-2022.173605, author={Tran Thi Kim Nga and Tuan Pham-Viet and Dang Minh Nhat and Dang Minh Tam and Insoo Koo and Vladimir Y. Mariano and Tuan Do-Hong}, title={Extending Color Properties for Texture Descriptor Based on Local Ternary Patterns to Classify Rice Varieties}, journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems}, volume={9}, number={30}, publisher={EAI}, journal_a={INIS}, year={2022}, month={3}, keywords={rice varieties, local ternary pattern, improved local ternary pattern, texture feature, support vector machine}, doi={10.4108/eai.7-3-2022.173605} }- Tran Thi Kim Nga
Tuan Pham-Viet
Dang Minh Nhat
Dang Minh Tam
Insoo Koo
Vladimir Y. Mariano
Tuan Do-Hong
Year: 2022
Extending Color Properties for Texture Descriptor Based on Local Ternary Patterns to Classify Rice Varieties
INIS
EAI
DOI: 10.4108/eai.7-3-2022.173605
Abstract
In this study, a proposed descriptor based on the improved local ternary patterns (ILTP) that also uses the color properties of rice varieties is presented. Not only gray-scale intensity, but R, G, and B color components of the rice grains are considered. Combining a support vector machine (SVM) with the proposed descriptor for classification of 17 rice varieties planted in Vietnam gives an overall accuracy of 95.53%. To evaluate and compare the effectiveness of the proposed descriptor with other analysis techniques for rice varieties classification, texture descriptors based on local binary pattern and local ternary patterns are combined with SVM to classify these 17 rice varieties. Experiment results show that the classification accuracy from the SVM using the proposed descriptor is significantly higher than using ILTP or other texture descriptors from the literature. This technique can be used to build an automatic system of rice varieties identification and classification.
Copyright © 2022 T. T. K. Nga et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
