Abstract—Texture features have always been a key attribute in image recognition and classification. In this paper we propose two pre-processing methods for enhancing the performance of widely used color texture recognition methods. In the first approach we propose decorrelation stretching for color enhancement, which is known to improve the interpretability of color images. The second method employs Cartoon-Texture decomposition for sharpening the texture component of the image. We show that both methods improve the classification accuracy by 7% and 4% respectively when applied to images prior to extracting auto and cross-correlation features. Our conclusion is that the proposed approach could be helpful in machine vision tasks.
Index Terms—Texture classification, decorrelation stretching, cartoon-texture decomposition, cross-correlation, sharpening.
Micha Kalfon is with the Computer Science Department, Technion, Haifa 32000, Israel (e-mail: smichaku@cs.technion.ac.il).
Moshe Porat is with the Electrical Engineering Department, Technion, Haifa 32000, Israel (e-mail: mp@ee.technion.ac.il).
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Cite: Micha Kalfon and Moshe Porat, "A New Approach to Texture Recognition Using Decorrelation Stretching,"
International Journal of Future Computer and Communication vol. 2, no. 1 pp. 49-53, 2013.
