SYSTEMATIZATION OF SPACE OF STRUCTURAL FEATURES BASED ON SELF-LEARNING METHODS FOR EFFECTIVE IMAGE RECOGNITION

V. A. Gorokhovatsky, A. E. Berestovskyi, Е. О. Peredrii

Abstract


The work deals with issues of clustering sets of characteristic features of images. For the construction of array of the characteristic features is used method Speeded Up Robust Features. Implemented algorithms for clustering structural descriptions of images on the basis of a self-organizing Kohonen neural network and method of grouping the difference. The object of the research are clustering methods which applied to the set of structural features. The aim is to construct a vector representations of descriptions based on clustering, which increases the speed of recognition. The subject of research is systematization a set of structural features of visual objects. Discussing the results of the application of clustering methods for structural descriptions of images in the form of sets of characteristic features to improve the performance of visual recognition of objects. For systematization and compression the feature space proposed to carry out self-study using the methods of differential grouping and Kohonen networks. The simulation and experimental study of clustering methods on examples of specific sets of characteristic features were done. The research results proves the possibility of effective representation of the descriptions in the form of a vector with integer elements. This approach can be used to solve problems of recognition and retrieval of images. As a result compact vector description of etalon images is built, quantitative estimates of clustering error are estimated, efficiency of proposed method during processing of real image database is confirmed.

Keywords


computer vision, image recognition, characteristic signs, structural description of image, method SURF, clusterization, neural network, differential grouping method, Kohonen’s neural network, quantization error.

References


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DOI: https://doi.org/10.15588/1607-3274-2016-2-11



Copyright (c) 2016 V. A. Gorokhovatsky, A. E. Berestovskyi, Е. О. Peredrii

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