Texture is generally recognized as fundamental to perceptions. There is no precise definition or characterization available in practice. Texture recognition has many applications in areas such as medical image analysis, remote sensing, and robotic vision. Various approaches such as statistical, structural, and spectral have been suggested in the literature. In this paper we propose a method for texture feature extraction. We transform the image into a two-dimensional Discrete Cosine Transform (DCT) and extract features using the ring and wedge bins in the DCT plane. These features are based on texture properties such as coarseness, smoothness, graininess, and directivity of the texture pattern in the image. We develop a model to classify texture images using extracted features. We use three classifiers: the Decision Tree, Support Vector Machine (SVM), and Logarithmic Regression (LR). To test our approach, we use Brodatz texture image data set consisting of 111 images of different texture patterns. Classification results such as accuracy and F-score obtained from the three classifiers are presented in the paper.


This article is originally published in the International Journal of Advanced Computer Science and Applications (IJACSA), vol. 12, No. 3, 2021, licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).


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Arun Kulkarni, Aavash Sthapit, Ashim Sedhain, Bishrut Bhattarai and Saurav Panthee, “Texture Classification using Angular and Radial Bins in Transformed Domain” International Journal of Advanced Computer Science and Applications(IJACSA), 12(3), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120301