Maximum Similarity Method for Image Mining

The paper discusses a new Image Mining approach to extracting and exploring relations in the image repositories. The proposed approach, called Maximum Similarity Method, is based on the identification of characteristic fragments in images by a set of predefined patterns. Such an identification is basically carried out as a comparison of the fragment intensity shape with the shapes of already registered patterns - precedents. Mathematically (statistically) such a comparison implies a selection of some measure of similarity and optimization (maximization) of that measure on a set of precedents. In the paper, basing on the principles of machine learning, a special type of similarity measure is proposed, and its reliability is discussed. In fact, this measure represents conditional probability distribution of the registered data - counts of a fragment tested when analogous data for the patterns are given. So, the search for the optimal precedent pattern that maximized the chosen similarity measure constitutes the proposed method.