A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion Optimizer
Texture classification is an important topic for many applications in machine vision and image analysis, and Gabor filter is considered one of the most efficient tools for analyzing texture features at multiple orientations and scales. However, the parameter settings of each filter are crucial for o...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/11/2173 |
| _version_ | 1818998166738960384 |
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| author | Mingwei Wang Lang Gao Xiaohui Huang Ying Jiang Xianjun Gao |
| author_facet | Mingwei Wang Lang Gao Xiaohui Huang Ying Jiang Xianjun Gao |
| author_sort | Mingwei Wang |
| collection | DOAJ |
| description | Texture classification is an important topic for many applications in machine vision and image analysis, and Gabor filter is considered one of the most efficient tools for analyzing texture features at multiple orientations and scales. However, the parameter settings of each filter are crucial for obtaining accurate results, and they may not be adaptable to different kinds of texture features. Moreover, there is redundant information included in the process of texture feature extraction that contributes little to the classification. In this paper, a new texture classification technique is detailed. The approach is based on the integrated optimization of the parameters and features of Gabor filter, and obtaining satisfactory parameters and the best feature subset is viewed as a combinatorial optimization problem that can be solved by maximizing the objective function using hybrid ant lion optimizer (HALO). Experimental results, particularly fitness values, demonstrate that HALO is more effective than the other algorithms discussed in this paper, and the optimal parameters and features of Gabor filter are balanced between efficiency and accuracy. The method is feasible, reasonable, and can be utilized for practical applications of texture classification. |
| first_indexed | 2024-12-20T21:57:13Z |
| format | Article |
| id | doaj.art-5d4906946ffd4b9ba363516a62697e9d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-20T21:57:13Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5d4906946ffd4b9ba363516a62697e9d2022-12-21T19:25:26ZengMDPI AGApplied Sciences2076-34172019-05-01911217310.3390/app9112173app9112173A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion OptimizerMingwei Wang0Lang Gao1Xiaohui Huang2Ying Jiang3Xianjun Gao4Institute of Geological Survey, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaChangjiang River Scientific Research Institute, Changjiang Water Resources Commission, Wuhan 430010, ChinaSchool of Geoscience, Yangtze University, Wuhan 430100, ChinaTexture classification is an important topic for many applications in machine vision and image analysis, and Gabor filter is considered one of the most efficient tools for analyzing texture features at multiple orientations and scales. However, the parameter settings of each filter are crucial for obtaining accurate results, and they may not be adaptable to different kinds of texture features. Moreover, there is redundant information included in the process of texture feature extraction that contributes little to the classification. In this paper, a new texture classification technique is detailed. The approach is based on the integrated optimization of the parameters and features of Gabor filter, and obtaining satisfactory parameters and the best feature subset is viewed as a combinatorial optimization problem that can be solved by maximizing the objective function using hybrid ant lion optimizer (HALO). Experimental results, particularly fitness values, demonstrate that HALO is more effective than the other algorithms discussed in this paper, and the optimal parameters and features of Gabor filter are balanced between efficiency and accuracy. The method is feasible, reasonable, and can be utilized for practical applications of texture classification.https://www.mdpi.com/2076-3417/9/11/2173texture classificationGabor filterparameter optimizationfeature selectionhybrid ant lion optimizer |
| spellingShingle | Mingwei Wang Lang Gao Xiaohui Huang Ying Jiang Xianjun Gao A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion Optimizer Applied Sciences texture classification Gabor filter parameter optimization feature selection hybrid ant lion optimizer |
| title | A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion Optimizer |
| title_full | A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion Optimizer |
| title_fullStr | A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion Optimizer |
| title_full_unstemmed | A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion Optimizer |
| title_short | A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion Optimizer |
| title_sort | texture classification approach based on the integrated optimization for parameters and features of gabor filter via hybrid ant lion optimizer |
| topic | texture classification Gabor filter parameter optimization feature selection hybrid ant lion optimizer |
| url | https://www.mdpi.com/2076-3417/9/11/2173 |
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