Unsupervised image segmentation using EM algorithm by histogram

Authors:
Zhi-Kai Huang;De-Hui Liu
Affiliations:
Department of Machinery and Dynamic Engineering, Nanchang Institute of Technology, Nanchang, Jiangxi, China;Department of Machinery and Dynamic Engineering, Nanchang Institute of Technology, Nanchang, Jiangxi, China
Venue:
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Year:
2007

Citing 2
Cited 2

Operations Useful for Similarity-Invariant Pattern Recognition

Journal of the ACM (JACM)
Digital Image Processing

Digital Image Processing

Video motion detection beyond reasonable doubt

Proceedings of the 1st international conference on Forensic applications and techniques in telecommunications, information, and multimedia and workshop
Histogram-based foreground object extraction for indoor and outdoor scenes

Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, an efficient approach to search for the global threshold of image using Gaussian mixture model is proposed. Firstly, a gray-level histogram of an image is represented as a function of the frequencies of gray-level. Then to fit the Gaussian mixtures to the histogram of image, the Expectation Maximisation (EM) algorithm is developed to estimate the number of Gaussian mixture of such histograms. Finally, the optimal threshold which is the average of these means is chosen. The paper compares the new method with the classical discriminate analysis method of Otsu's. And the experimental results show that the new algorithm performs better than that of Otsu's.