Comparative analysis of texture feature extraction techniques for rice grain classification
Classifications of eight different varieties of rice grain are discussed in this study based on various texture models. Four local texture feature extraction techniques are proposed and three sets of texture features (SET‐A, SET‐B and SET‐C) are formed, for the classification task. Performances of t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-09-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-ipr.2019.1055 |
| _version_ | 1818652843846926336 |
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| author | Kshetrimayum Robert Singh Saurabh Chaudhury |
| author_facet | Kshetrimayum Robert Singh Saurabh Chaudhury |
| author_sort | Kshetrimayum Robert Singh |
| collection | DOAJ |
| description | Classifications of eight different varieties of rice grain are discussed in this study based on various texture models. Four local texture feature extraction techniques are proposed and three sets of texture features (SET‐A, SET‐B and SET‐C) are formed, for the classification task. Performances of the proposed feature sets are compared with the existing techniques based on, run length matrix, co‐occurrence matrix, size zone matrix, neighbourhood grey tone difference matrix and wavelet decomposition, towards classification of rice grain using a back propagation neural network (BPNN). The proposed techniques are also tested against publicly available data from Brodatz's texture data set and their results are compared with other techniques. The classification accuracy by the BPNN classifier is also compared with other statistical classifiers namely, K‐nearest neighbour, linear discriminant classifier and Naive Bayes classifier. It is found that, the proposed feature sets yield better classification results on both rice data and Brodatz's data. Results show that, feature SET‐B, is able to classify rice grain with an average classification accuracy of 99.63% with a minimum of six features. |
| first_indexed | 2024-12-17T02:28:27Z |
| format | Article |
| id | doaj.art-9104fd2add0542c4803331518c564a50 |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-12-17T02:28:27Z |
| publishDate | 2020-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-9104fd2add0542c4803331518c564a502022-12-21T22:07:02ZengWileyIET Image Processing1751-96591751-96672020-09-0114112532254010.1049/iet-ipr.2019.1055Comparative analysis of texture feature extraction techniques for rice grain classificationKshetrimayum Robert Singh0Saurabh Chaudhury1Electrical Engineering DepartmentNational Institute of Technology SilcharSilchar708810IndiaElectrical Engineering DepartmentNational Institute of Technology SilcharSilchar708810IndiaClassifications of eight different varieties of rice grain are discussed in this study based on various texture models. Four local texture feature extraction techniques are proposed and three sets of texture features (SET‐A, SET‐B and SET‐C) are formed, for the classification task. Performances of the proposed feature sets are compared with the existing techniques based on, run length matrix, co‐occurrence matrix, size zone matrix, neighbourhood grey tone difference matrix and wavelet decomposition, towards classification of rice grain using a back propagation neural network (BPNN). The proposed techniques are also tested against publicly available data from Brodatz's texture data set and their results are compared with other techniques. The classification accuracy by the BPNN classifier is also compared with other statistical classifiers namely, K‐nearest neighbour, linear discriminant classifier and Naive Bayes classifier. It is found that, the proposed feature sets yield better classification results on both rice data and Brodatz's data. Results show that, feature SET‐B, is able to classify rice grain with an average classification accuracy of 99.63% with a minimum of six features.https://doi.org/10.1049/iet-ipr.2019.1055comparative analysistexture feature extraction techniquesrice grain classificationvarious texture modelslocal texture feature extraction techniquesrun length matrix |
| spellingShingle | Kshetrimayum Robert Singh Saurabh Chaudhury Comparative analysis of texture feature extraction techniques for rice grain classification IET Image Processing comparative analysis texture feature extraction techniques rice grain classification various texture models local texture feature extraction techniques run length matrix |
| title | Comparative analysis of texture feature extraction techniques for rice grain classification |
| title_full | Comparative analysis of texture feature extraction techniques for rice grain classification |
| title_fullStr | Comparative analysis of texture feature extraction techniques for rice grain classification |
| title_full_unstemmed | Comparative analysis of texture feature extraction techniques for rice grain classification |
| title_short | Comparative analysis of texture feature extraction techniques for rice grain classification |
| title_sort | comparative analysis of texture feature extraction techniques for rice grain classification |
| topic | comparative analysis texture feature extraction techniques rice grain classification various texture models local texture feature extraction techniques run length matrix |
| url | https://doi.org/10.1049/iet-ipr.2019.1055 |
| work_keys_str_mv | AT kshetrimayumrobertsingh comparativeanalysisoftexturefeatureextractiontechniquesforricegrainclassification AT saurabhchaudhury comparativeanalysisoftexturefeatureextractiontechniquesforricegrainclassification |