Research of data space transformation in Kohonen network training for methods of structural classification of images
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Abstract
The subject of the article's research is the modification of Kohonen neural network training for image classification. The purpose is to define a new data space for network learning and to create an effective classification method based on a set of key point descriptors. Task: use of the Kohonen neural network to train the classification system in a certain data space, transform the data space of the training network, study adaptive capabilities and evaluate the effectiveness of the network by means of software modeling. The methods are: data mining, structural classification of images, ORB detector for determining key point descriptors, Kohonen network learning tools, software modeling. The following results were obtained: data transformation models that enhance learning performance are proposed. The comparative analysis of the developed methods of training and classification is made. A software implementation of the classification system has been carried out, its efficiency has been experimentally investigated and the processing time has been evaluated. Conclusions. The scientific novelty of the study is refining structural classification methods using Kohonen network by introducing a new center based clustering approach and a method for collapsing descriptor data, which contributes to high classification performance at sufficient speed and enables the use of these modified methods in real-time applications. The practical importance of the work is created by the models of software used to evaluate the performance of the classifiers in computer vision systems, and the proven effectiveness of the development on the specified image databases.
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References
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