Abstract
Experimental evaluation of 12 nonparametric clustering algorithms for image segmentation was made. Algorithms developed in FRC ICT are compared to ones from ENVI, ELKI and Smile software packages. Seven model datasets were generated to estimate clustering accuracy. The computational efficiency was evaluated using digital photographs and fragments of multispectral images obtained from WorldView-2 satellite.
Highlights
Image segmentation is required for solution of a number of applied problems
An experimental comparison of these algorithms and six most popular clustering algorithms implemented in ENVI [5], ELKI [6] and Smile [7] software packages is performed
Except for DBSCAN and OPTICS, allowed to obtain a clustering accuracy of about 85% for model dataset No 5
Summary
Image segmentation is required for solution of a number of applied problems These can be multispectral images obtained from satellites, aircrafts or unmanned aerial vehicles, as well as conventional digital photographs (e.g. medical examinations data). One of the most common approaches to image segmentation is based on the use of data clustering algorithms [2]. Image segmentation is usually performed with neither a priori information about the probabilistic characteristics of classes, nor training samples. In these conditions, the most suitable is nonparametric approach to clustering [3]. The use of the grid-based approach makes it possible to achieve high computational efficiency due to processing relatively small number of cells instead of data elements. An experimental comparison of these algorithms and six most popular clustering algorithms implemented in ENVI [5], ELKI [6] and Smile [7] software packages is performed
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