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The article considers the problem of image recognition in computer vision systems. The results of the development of the method for image classification, using a structural approach, are presented. The classification method is based on calculating the values of statistical distributions for the set of description descriptors. The distribution vector for a fixed set of classes is based on the calculation of the degree of similarity with the integral characteristics for the descriptions of the etalon base. Two options for constructing the classifier on the principles of object &#x2013; etalon and object descriptor &#x2013; etalon, which differ in the degree of integration of the solution, are proposed. The median for the set of vectors describing the etalon is used as the aggregate characteristic of the etalon descriptions. The experimental evaluation of the effectiveness of the developed classifiers in terms of verification of performance and evaluation of the probability of correct classification according to the results of processing of applied images based on three etalons are carried out. The values of precision and completeness indicators for the method object descriptor &#x2013; etalon, which has demonstrated the significant advantage over the integrated approach, are given. At the same time, both proposed in the experiment methods classify the set of etalons without error. The methods of mathematical statistics, intellectual data analysis, image recognition, the apparatus for calculating the relevance of the system of the features, as well as simulation modelling, are used in this research. Based on the study and the experiment, it was found that the processing time of the images for the developed method is approximately 7 times less than for the traditional method, without reducing the accuracy. The perspective of further research is to study the interference immunity of the developed methods and evaluate their applied effectiveness for three-dimensional image collections.

Methods of Classification of Images on the Basis of the Values of Statistical Distributions for the Composition of Structural Description Components