Video Image Segmentation Using Gaussian Mixture Models Based on the Differential Evolution-Based Parameter Estimation

Abstract:

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In order to improve the accuracy of the image segmentation in video surveillance sequences and to overcome the limits of the traditional clustering algorithms that can not accurately model the image data sets which Contains noise data, the paper presents an automatic and accurate video image segmentation algorithm, according to the spatial properties, which uses the Gaussian mixture models to segment the image. But the expectation-maximization algorithm is very sensitive to initial values, and easy to fall into local optimums, so the paper presents a differential evolution-based parameters estimation for Gaussian mixture models. The experiment result shows that the segmentation accuracy has been improved greatly than by the traditional segmentation algorithms.

Info:

Periodical:

Key Engineering Materials (Volumes 474-476)

Edited by:

Garry Zhu

Pages:

442-447

DOI:

10.4028/www.scientific.net/KEM.474-476.442

Citation:

Z. G. Zeng et al., "Video Image Segmentation Using Gaussian Mixture Models Based on the Differential Evolution-Based Parameter Estimation", Key Engineering Materials, Vols. 474-476, pp. 442-447, 2011

Online since:

April 2011

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Price:

$35.00

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