Entropy Approach to Comparison of Images
Eugenia N. Kirsanova, Michael G. Sadovsky · Open Systems & Information Dynamics · 2001
Basically new pattern recognition method is implemented to compare two (or several) digital images. The method has neither feature alphabet, nor pattern dictionary recovery stages. It compares input images due to a special object called palette built from fragments of images. The measures to estimate the distances between images are based on a determination of the specific entropy of the frequency dictionary of an image with respect to the palette; that latter presents the statistical ancestor of the group of the images under comparison. The palette is defined as the frequency dictionary with frequencies of the fragments equal to arithmetic mean of the frequencies of the same fragments from the images to be compared. Such definition yields a minimum of the sum of specific entropies of the compared images with respect to the palette. Some preliminary results in the application of the method in pattern recognition and synergistics problems are presented. The limitations and basic properties of the method are discussed.