1 April 1997 Backpropagation neural network for adaptive color image segmentation
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Abstract
A practical approach to continuous-tone color image segmentation is proposed. Unlike traditional algorithms of image segmentation which tend to use threshold methods we intend to show how neural network technique can be successfully applied to this problem. We used a back- propagation network architecture in this work. It was assumed that each image pixel has its own color, which is somehow correlated with those of the nearest neighborhood. To describe the color properties of a certain neighborhood we suggested nine component feature vectors for every image pixel. This set of feature components is applied to the network input neurons. By this means, every image pixel is described by the following values R, G and B (color intensities), Mr, Mg and Mb (averages of intensities of the nearest neighborhood), (sigma) r, (sigma) gland (sigma) b (r.m.s. deviations of color intensities). To estimate the algorithm efficiency the scalar criterion was proposed. It was shown by the results of comparative experiment that neural segmentation provides more efficiency than that of traditional, using threshold methods.
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Sergey N. Krjukov, Sergey N. Krjukov, Tatjana Olegovna Semenkova, Tatjana Olegovna Semenkova, Valerija A. Pavlova, Valerija A. Pavlova, Boris Ivanovitch Arnt, Boris Ivanovitch Arnt, "Backpropagation neural network for adaptive color image segmentation", Proc. SPIE 3030, Applications of Artificial Neural Networks in Image Processing II, (1 April 1997); doi: 10.1117/12.269777; https://doi.org/10.1117/12.269777
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