Abstract
In the recent years, many authors have begun to exploit the extra information provided by color images to solve many computer vision problems. Among these problems, we find the texture classification field, which traditionally has used grayscale images, primarily due to the high hardware and processing costs. In this paper, a new approach for enhancing classical texture analysis methods is presented. By means of the band ratioing technique, we can extend any feature extraction algorithm to take advantage of color information and achieve higher classification rates. To prove this extreme, three standard techniques has been selected: Gabor filters, Wavelets and Cooccurrence Matrices. For testing purposes, 30 color textures have been selected from the Vistex database. We will perform a number of experiments on that texture set, combining different ways of adapting the former algorithms to process color textures and extract features from them.
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Muñiz, R., Corrales, J.A. (2003). Use of Band Ratioing for Color Texture Classification. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_71
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DOI: https://doi.org/10.1007/978-3-540-44871-6_71
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