Abstract
This paper proposes a computational scheme for fuzzy similarity analysis and classification of images. First, a special growing unsupervised learning algorithm is introduced for Information Granulation of the original "raw data" (the RGB pixels) of the image with much smaller number of information granules (neurons). Then two features are extracted from each image, as follows: the Center-of-Gravity of the image and the Image Volume (as number of neurons). These features are used as inputs of a special fuzzy inference procedure that computes numerically the similarity degree between a given pair of images. Finally, a sorting procedure with a predefined threshold is used to obtain the final classification results for all available images. The proposed classification scheme is illustrated on the example of 18 images of flowers. It is also shown in the paper that by appropriate tuning of the parameters of the Fuzzy Inference procedure, high plausibility of the classification can be achieved.
