Gabor Contrast Patterns: A Novel Framework to Extract Features From Texture Images
In this paper, a novel rotation and scale invariant approach for texture classification based on Gabor filters has been proposed. These filters are designed to capture the visual content of the images based on their impulse responses which are sensitive to rotation and scaling in the images. The fil...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10135089/ |
| _version_ | 1797797242358202368 |
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| author | Abdul Wahab Muzaffar Farhan Riaz Tarik Abuain Waleed Abdel Karim Abu-Ain Farhan Hussain Muhammad Umar Farooq Muhammad Ajmal Azad |
| author_facet | Abdul Wahab Muzaffar Farhan Riaz Tarik Abuain Waleed Abdel Karim Abu-Ain Farhan Hussain Muhammad Umar Farooq Muhammad Ajmal Azad |
| author_sort | Abdul Wahab Muzaffar |
| collection | DOAJ |
| description | In this paper, a novel rotation and scale invariant approach for texture classification based on Gabor filters has been proposed. These filters are designed to capture the visual content of the images based on their impulse responses which are sensitive to rotation and scaling in the images. The filter responses are rearranged according to the filter exhibiting the response having largest amplitude, followed by the calculation of patterns after binarizing the responses based on a particular threshold. This threshold is obtained as the average energy of Gabor filter responses at a particular pixel. The binary patterns are converted to decimal numbers, the histograms of which are used as texture features. The proposed features are used to classify the images from two famous texture datasets: Brodatz, CUReT and UMD texture albums. Experiments show that the proposed feature extraction method performs really well when compared with several other state-of-the-art methods considered in this paper and is more robust to noise. |
| first_indexed | 2024-03-13T03:45:13Z |
| format | Article |
| id | doaj.art-0f3cdccdfdea494fb959e72ddf0c996a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T03:45:13Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0f3cdccdfdea494fb959e72ddf0c996a2023-06-22T23:00:26ZengIEEEIEEE Access2169-35362023-01-0111603246033410.1109/ACCESS.2023.328005310135089Gabor Contrast Patterns: A Novel Framework to Extract Features From Texture ImagesAbdul Wahab Muzaffar0https://orcid.org/0000-0001-7910-0378Farhan Riaz1Tarik Abuain2https://orcid.org/0000-0002-8023-7600Waleed Abdel Karim Abu-Ain3Farhan Hussain4Muhammad Umar Farooq5https://orcid.org/0000-0003-1520-0753Muhammad Ajmal Azad6College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi ArabiaSchool of Computer Science, University of Lincoln, Lincoln, U.KCollege of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi ArabiaApplied College, Taibah University, Al Madinah Al Munawwarah, Saudi ArabiaDepartment of Computer and Software Engineering, National University of Sciences and Technology, Islamabad, PakistanDepartment of Computer and Software Engineering, National University of Sciences and Technology, Islamabad, PakistanDepartment of Computer Science and Digital Technologies, Birmingham City University, Birmingham, U.KIn this paper, a novel rotation and scale invariant approach for texture classification based on Gabor filters has been proposed. These filters are designed to capture the visual content of the images based on their impulse responses which are sensitive to rotation and scaling in the images. The filter responses are rearranged according to the filter exhibiting the response having largest amplitude, followed by the calculation of patterns after binarizing the responses based on a particular threshold. This threshold is obtained as the average energy of Gabor filter responses at a particular pixel. The binary patterns are converted to decimal numbers, the histograms of which are used as texture features. The proposed features are used to classify the images from two famous texture datasets: Brodatz, CUReT and UMD texture albums. Experiments show that the proposed feature extraction method performs really well when compared with several other state-of-the-art methods considered in this paper and is more robust to noise.https://ieeexplore.ieee.org/document/10135089/Texture classificationGabor filterspattern recognition |
| spellingShingle | Abdul Wahab Muzaffar Farhan Riaz Tarik Abuain Waleed Abdel Karim Abu-Ain Farhan Hussain Muhammad Umar Farooq Muhammad Ajmal Azad Gabor Contrast Patterns: A Novel Framework to Extract Features From Texture Images IEEE Access Texture classification Gabor filters pattern recognition |
| title | Gabor Contrast Patterns: A Novel Framework to Extract Features From Texture Images |
| title_full | Gabor Contrast Patterns: A Novel Framework to Extract Features From Texture Images |
| title_fullStr | Gabor Contrast Patterns: A Novel Framework to Extract Features From Texture Images |
| title_full_unstemmed | Gabor Contrast Patterns: A Novel Framework to Extract Features From Texture Images |
| title_short | Gabor Contrast Patterns: A Novel Framework to Extract Features From Texture Images |
| title_sort | gabor contrast patterns a novel framework to extract features from texture images |
| topic | Texture classification Gabor filters pattern recognition |
| url | https://ieeexplore.ieee.org/document/10135089/ |
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