Comparison of Tensor Voting based clustering and EM based clustering

Author

Madhubalan, Kavitha ; Lee, Gueesang

Author_Institution

Dept. of Electron. & Comput. Eng., Chonnam Nat. Univ., Kwangju, South Korea

fYear

2011

fDate

9-11 Feb. 2011

Firstpage

1

Lastpage

5

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.

Keywords

Gaussian noise; expectation-maximisation algorithm; image colour analysis; image segmentation; pattern clustering; tensors; Gaussian noise; dominant colors; expectation maximization clustering; image segmentation technique; k-means algorithm; mean shift algorithm; tensor voting based clustering; Algorithm design and analysis; Clustering algorithms; Image color analysis; Image segmentation; Noise; Robustness; Tensile stress; K-means algorithm; Mean shift algorithm; Tensor voting; color image segmentation; expectation maximization algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Frontiers of Computer Vision (FCV), 2011 17th Korea-Japan Joint Workshop on

Conference_Location

Ulsan

Print_ISBN

978-1-61284-677-4

Electronic_ISBN

978-1-61284-676-7

Type

conf

DOI

10.1109/FCV.2011.5739740

Filename

5739740