Comparison of Tensor Voting based clustering and EM based clustering

Abstract

Comparison of image segmentation techniques based on the number of dominant colors and clusters is presented. Tensor Voting, Expectation Maximization algorithm, K-Means Algorithm and Mean Shift Algorithm are considered. The image segmentation results are analyzed with constant and varying number of clusters for all algorithms. Finally the performance of all algorithms under Gaussian noise is also evaluated. Performance results suggest that Tensor Voting based segmentation is more robust to noise compared to other techniques.