DOI: https://doi.org/10.15588/1607-3274-2020-4-9

CLASSIFICATION OF IMAGES BASED ON AN ENSEMBLE OF STATISTICAL DISTRIBUTIONS BY CLASSES OF ETALONS FOR STRUCTURAL DESCRIPTION COMPONENTS

V. A. Gorokhovatsky, S. V. Gadetska, N. I. Stiahlyk, N. V. Vlasenko

Abstract


Context. Modern computer vision systems require effective classification solutions based on in-depth analysis of the nature of the data being processed. Statistical distributions are currently the primary means of analysis in image recognition systems. If the description of the recognized object is given by a large number of vectors, the statistical apparatus becomes a fundamental way to effectively decide on the class of the recognized object. This requires the use of a universal distribution apparatus in general for a system of multidimensional descriptions for established classes of data, defined by a given database of etalons. The classifier creates or organizes a new spatial structure of vectors from the elements of the analyzed object, which generally has some estimated similarity to the structure or composition of the etalon elements, and the classification is done by optimizing the degree of this similarity on the set of etalons. The probabilistic model of data generation is a key practical approach to formalizing the task of classifier training, the essence of which is to establish statistical distributions of objects or their components, followed by the procedure of aggregation of component solutions and further optimization in the environment of etalon classes. It is also valuable to study and apply criteria for evaluating the effectiveness in classification problem based on statistical principles.

Objective. Development of a method of effective classification of images by introduction of ensemble statistical decisions for structure of components of the description.

Method. A method for classifying images based on the construction of a generalized solution of an ensemble of components for which statistical distributions by data classes are preliminarily calculated is proposed.

Results. The synthesis of the classification method by applying the ensemble solution of the components of the description is carried out. The efficiency and effectiveness of the developed classifier are confirmed. On the examples of application of the method for synthesized data using traditional criteria, its effectiveness was experimentally evaluated.

Conclusions. The investigated methods of constructing an image classifier are based on an ensemble of partial solutions of statistical analysis data for the components of the structural description in the form of a set of key point descriptors. The statistical approach provides identification of the priority classification decision for components of the description on which set the resulting decision of ensemble is formed.

The scientific novelty of the study is the development of image classification method based on an ensemble of solutions of the component description, based on their statistical distributions by data classes.

The practical significance of the work lies in confirming the efficiency and effectiveness of the proposed methods on demonstration examples.


Keywords


Computer vision, methods of structural image recognition, set of key points, ORB descriptor, description components, statistical distribution, ensemble of solutions, classification efficiency criterion.

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