Classification of images based on information compression and fuzzy rule based similarity analysis

This paper proposes a computational scheme for fuzzy similarity analysis and classification of images by comparison of the new (unknown) images with a predetermined number of known (core) images, contained in an image base. As a first step, an unsupervised competitive learning algorithm is used to create the so called compressed information model (CIM) which replaces the original ldquoraw datardquo (the RGB pixels) of the image with much smaller number of neurons. Then two specially introduced parameters of the CIM are computed, namely the center-of-gravity of the model and the generalized model size. These parameters are used as inputs of a special fuzzy inference procedure that computes numerically the similarity between a given pair if images as a difference degree between them. Finally, a sorting procedure with a predefined threshold is used to obtain the results from the classification. The flexibility and applicability of the whole proposed unsupervised classification scheme is illustrated on the example of classification of 18 different images by use of three different image bases containing, 3, 5 and 7 ldquocorerdquo images respectively.