Image classification based on the kohonen network and the data space modification
Volodymyr Gorokhovatskyi, Iryna Tvoroshenko · 2020
In this paper, we propose the solution of visual objects recognition in computer vision problems using the classification of descriptors of image keypoints based on the training of Kohonen neural network on the description data of etalon images.According to the results of training within the set of etalons, the image classification method has been improved by defining a specific data space in the form of a statistical center for each etalon.We propose mathematical models for the bitwise analysis of multiple descriptors searching for the centers and the method for convolution of descriptions from multiple descriptors with the determining a posteriori probabilities for the system of bit centers.Methods of data space transformation of description bits are proposed for various options for Kohonen network training, processing and estimation of class centers.The software implementation of the changed classifier was performed as well as the processing time with different options for determining the space of training data was estimated.Experimental researches confirmed the high efficiency of classification preserving sufficient performance and the ability to use proposed methods in real-time applications.