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Computational scheme for comparison, color analysis and segmentation of images is proposed in this paper. First of all, two growing unsupervised learning algorithms are introduced. They create the so called compressed information model (CIM) of the image that replaces the original ldquoraw datardquo (the RGB pixels) with a smaller number of neurons. Then two main features are extracted from the CIM,...
This paper proposes a computational scheme for fuzzy similarity analysis and classification of images that uses first an information granulation procedure followed by a subsequent fuzzy decision procedure. A special new version of the growing unsupervised learning algorithm is introduced in the paper for information granulation. It reduces the original ldquoraw datardquo (the RGB pixels) of the image...
This paper proposes a computational scheme for comparison and color analysis of images by using unsupervised learning algorithms. As a first step, two special growing unsupervised learning algorithms are introduced and used to create the so called compressed information model (CIM) which replaces the original ldquoraw datardquo (the RGB pixels) of the image with a much smaller number of neurons. Then...
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