Comparison, Segmentation and Analysis of Images by Use of Information Compression Algorithms

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, namely the center-of-gravity of the model and the weighted average size. They could be used separately or in a two-dimensional fuzzy decision block for similarity analysis of pairs of images. Another type of image analysis is the image segmentation. A simple method for segmentation is described in the paper. It uses the unsupervised learning algorithms to generate small predetermined number of neurons (key-points of the image). Each key-point is considered as a center of the respective image segment and the area of pixels around this center corresponds to the color details of this segment. The proposed computational scheme and its application are demonstrated in the paper on test image examples.