COMPARISON OF CLUSTER ANALYSIS ALGORITHMS IN OBJECT RECOGNITION

Abstract

The article is an overview of the direction of graphic image processing based on clustering algorithms. The analysis of prospects of application of algorithms of cluster analysis in digital image processing, in particular, at segmentation and compression of graphic images, and also at recognition of images in transport sphere of activity is carried out. Comparative modeling of such algorithms of cluster analysis as K-means, Mean-Shift (clustering of average shift) and DBSCAN (based on density of spatial clustering for applications with noise) on various types of data is carried out. The simulation was performed on synthetic datasets in a Jupyter Notebook environment using the Scikit-learn library. In particular, four data sets were generated in this environment, to which these clustering algorithms were applied. The simulation results showed that the K-means algorithm can effectively describe relatively simple shapes. In contrast, the mean shift does not require assumptions about the number of clusters and the shape of the distribution, but its performance depends on the choice of scale parameters. The DBSCAN algorithm can successfully detect more complex shapes, which emphasizes one of the strengths of this algorithm - the clustering of arbitrary data. The disadvantages of the selected algorithms are also given and it is indicated on which types of images they effectively work with the estimation of computational speed.