4 March 1996 Self-organized neural network approach to color image separation
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Abstract
A practical approach to continuous-tone color image transformation into the image with a finite number of colors is proposed. To achieve this the self organized network was explored. The basic feature of this network provides its self-learning by the competition between several hypotheses (colors) about the analyzed image and the most authentic hypothesis overcomes. Unlike traditional algorithms of color separation which tend to analyze the quantitative contribution of different color components (red, green, blue) to the initial image, the proposed network moreover takes into consideration the qualitative, statistical character of their distribution. To make the operation of self training in learning mode for this network more accurate the output neurons are connected additionally by lateral excitement connections. We proposed the estimate to measure the algorithm efficiency and similarity between input and output images, which gives the possibility to compare our method with other well-known methods. Our method is useful in the fields of precision color image analysis and understanding.
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Sergey N. Krjukov, Sergey N. Krjukov, Valerija A. Pavlova, Valerija A. Pavlova, Tatjana Olegovna Semenkova, Tatjana Olegovna Semenkova, } "Self-organized neural network approach to color image separation", Proc. SPIE 2664, Applications of Artificial Neural Networks in Image Processing, (4 March 1996); doi: 10.1117/12.234262; https://doi.org/10.1117/12.234262
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