A new image thresholding method based on Gaussian mixture model

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 expec...

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Bibliographic Details
Published inApplied mathematics and computation Vol. 205; no. 2; pp. 899 - 907
Main Authors Huang, Zhi-Kai, Chau, Kwok-Wing
Format Journal Article Conference Proceeding
LanguageEnglish
Published Amsterdam Elsevier Inc 15.11.2008
Elsevier
Subjects
Calculus of variations and optimal control
Exact sciences and technology
Histogram
Mathematical analysis
Mathematics
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in mathematical programming, optimization and calculus of variations
Optimization
Sciences and techniques of general use
Thresholding
Histogram
Thresholding
Optimization
Experimental result
Numerical analysis
Applied mathematics
Optimization method
Gauss method
Parameterization
Algorithm
Mathematical programming
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Summary: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 maximization (EM) algorithm is developed to estimate the number of Gaussian mixture of such histograms and their corresponding parameterization. Finally, the optimal threshold which is the average of these Gaussian mixture means is chosen. And the experimental results show that the new algorithm performs better.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2008.05.130