16 June 1995 Texture classifiers using the surface area distribution of images
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Abstract
Texture is an important feature of images and it has been widely used for image analysis. Peleg has proposed that the fractal signature model can be used for classifying textured images. Fractal signature is the slope of measured area of a gray-level surface with changing resolution. To classify the textured image he suggested to compare fractal signatures in terms of a weighted mean squared error measure. In this paper, we propose that the area distribution with several similarity measures rather than its slope (fractal signature) can also be used for the same purpose. The area distribution of image is fed directly to the input of the classifier. Depending on how to design the structure of the classifier and what similarity measure to choose, the classification ratio is much different. We compare the methods, and show that as high as 83-93% correct classification ratio can be achieved. The texture pictures in this paper are taken from Brodatz. We find the best performance can be achieved when the area distribution rather than fractal signature is used with the two-layer perceptron classifier trained with the back-propagation algorithm.
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Joon-Cheol Kim, Joon-Cheol Kim, Kyoung-Bae Eum, Kyoung-Bae Eum, Joonwhoan Lee, Joonwhoan Lee, } "Texture classifiers using the surface area distribution of images", Proc. SPIE 2488, Visual Information Processing IV, (16 June 1995); doi: 10.1117/12.211997; https://doi.org/10.1117/12.211997
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