Weighted Constraint Feature Selection of Local Descriptor for Texture Image Classification
There are several statistical descriptors for feature extraction from texture images. Local binary pattern is one of the most popular descriptors for revealing the underlying structure of a texture. Recently several variants of local binary descriptors have been proposed. The completed local binary...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10223038/ |
| _version_ | 1797692147416170496 |
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| author | Entessar Saeed Gemeay Farhan A. Alenizi Adil Hussein Mohammed Mohammad Hossein Shakoor Reza Boostani |
| author_facet | Entessar Saeed Gemeay Farhan A. Alenizi Adil Hussein Mohammed Mohammad Hossein Shakoor Reza Boostani |
| author_sort | Entessar Saeed Gemeay |
| collection | DOAJ |
| description | There are several statistical descriptors for feature extraction from texture images. Local binary pattern is one of the most popular descriptors for revealing the underlying structure of a texture. Recently several variants of local binary descriptors have been proposed. The completed local binary pattern is an efficient version that can provide discriminant features and consequently provide a high classification rate. It finely characterizes a texture by fusing three histograms of features. Fusing histograms is applied by jointing the histograms and it increases the feature number significantly; therefore, in this paper, a weighted constraint feature selection approach is proposed to select a very small number of features without any degradation in classification accuracy. It significantly enhances the classification rate by using a very low number of informative features. The proposed feature selection approach is a filter-based feature selection. It employed a weighted constraint score for each feature. After ranking the features, a threshold estimation method is proposed to select the most discriminant features. For a better comparison, a wide range of different datasets is used as a benchmark to assess the compared methods. Implementations on Outex, UIUC, CUReT, MeasTex, Brodatz, Virus, Coral Reef, and ORL face datasets indicate that the proposed method can provide high classification accuracy without any learning step just by selecting a few features of the descriptor. |
| first_indexed | 2024-03-12T02:24:24Z |
| format | Article |
| id | doaj.art-bce3333050ef4c8c8f26b34246f6cc31 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T02:24:24Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bce3333050ef4c8c8f26b34246f6cc312023-09-05T23:01:22ZengIEEEIEEE Access2169-35362023-01-0111916739169510.1109/ACCESS.2023.330607510223038Weighted Constraint Feature Selection of Local Descriptor for Texture Image ClassificationEntessar Saeed Gemeay0https://orcid.org/0000-0002-2339-7229Farhan A. Alenizi1Adil Hussein Mohammed2https://orcid.org/0000-0002-6531-2051Mohammad Hossein Shakoor3https://orcid.org/0000-0001-8672-5181Reza Boostani4https://orcid.org/0000-0003-0055-4452Department of Computer Engineering, Computer and Information Technology College, Taif University, Taif, Saudi ArabiaElectrical Engineering Department, College of Engineering, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil, Kurdistan Region, Erbil, IraqDepartment of Computer Engineering, Faculty of Engineering, Arak University, Arak, IranComputer Engineering Department, School of Electrical and Computer Engineering, Shiraz University, Shiraz, IranThere are several statistical descriptors for feature extraction from texture images. Local binary pattern is one of the most popular descriptors for revealing the underlying structure of a texture. Recently several variants of local binary descriptors have been proposed. The completed local binary pattern is an efficient version that can provide discriminant features and consequently provide a high classification rate. It finely characterizes a texture by fusing three histograms of features. Fusing histograms is applied by jointing the histograms and it increases the feature number significantly; therefore, in this paper, a weighted constraint feature selection approach is proposed to select a very small number of features without any degradation in classification accuracy. It significantly enhances the classification rate by using a very low number of informative features. The proposed feature selection approach is a filter-based feature selection. It employed a weighted constraint score for each feature. After ranking the features, a threshold estimation method is proposed to select the most discriminant features. For a better comparison, a wide range of different datasets is used as a benchmark to assess the compared methods. Implementations on Outex, UIUC, CUReT, MeasTex, Brodatz, Virus, Coral Reef, and ORL face datasets indicate that the proposed method can provide high classification accuracy without any learning step just by selecting a few features of the descriptor.https://ieeexplore.ieee.org/document/10223038/Local binary patternweighted constraint feature selectiontexture image classification |
| spellingShingle | Entessar Saeed Gemeay Farhan A. Alenizi Adil Hussein Mohammed Mohammad Hossein Shakoor Reza Boostani Weighted Constraint Feature Selection of Local Descriptor for Texture Image Classification IEEE Access Local binary pattern weighted constraint feature selection texture image classification |
| title | Weighted Constraint Feature Selection of Local Descriptor for Texture Image Classification |
| title_full | Weighted Constraint Feature Selection of Local Descriptor for Texture Image Classification |
| title_fullStr | Weighted Constraint Feature Selection of Local Descriptor for Texture Image Classification |
| title_full_unstemmed | Weighted Constraint Feature Selection of Local Descriptor for Texture Image Classification |
| title_short | Weighted Constraint Feature Selection of Local Descriptor for Texture Image Classification |
| title_sort | weighted constraint feature selection of local descriptor for texture image classification |
| topic | Local binary pattern weighted constraint feature selection texture image classification |
| url | https://ieeexplore.ieee.org/document/10223038/ |
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