Gabor Contrast Patterns: A Novel Framework to Extract Features From Texture Images

Muzaffar, Abdul Wahab and Riaz, Farhan and Abuain, Tarik and Abu-Ain, Waleed Abdel Karim and Hussain, Farhan and Farooq, Muhammad Umar and Azad, Muhammad Ajmal (2023) Gabor Contrast Patterns: A Novel Framework to Extract Features From Texture Images. IEEE Access, 11. pp. 60324-60334. ISSN 2169-3536

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Abstract

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.

Item Type:	Article
Identification Number:	10.1109/ACCESS.2023.3280053
Dates:	Date Event 1 May 2023 Accepted 25 May 2023 Published Online
Uncontrolled Keywords:	Texture classification, Gabor filters, pattern recognition
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Gemma Tonks
Date Deposited:	26 Jun 2024 13:52
Last Modified:	26 Jun 2024 13:52
URI:	https://www.open-access.bcu.ac.uk/id/eprint/15601

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