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Color Image Segmentation Based on Morphological Operation and a Gaussian Mixture Model  

Lee Myung-Eun (Department of Electronics Engineering, Mokpo National University)
Park Soon-Young (Department of Electronics Engineering, Mokpo National University)
Cho Wan-Hyun (Department of Statistics, Chonnam National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.43, no.3, 2006 , pp. 84-91 More about this Journal
Abstract
In this paper, we present a new segmentation algorithm for color images based on mathematical morphology and a Gaussian mixture model(GMM). We use the morphological operations to determine the number of components in a mixture model and to detect their modes of each mixture component. Next, we have adopted the GMM to represent the probability distribution of color feature vectors and used the deterministic annealing expectation maximization (DAEM) algorithm to estimate the parameters of the GMM that represents the multi-colored objects statistically. Finally, we segment the color image by using posterior probability of each pixel computed from the GMM. The experimental results show that the morphological operation is efficient to determine a number of components and initial modes of each component in the mixture model. And also it shows that the proposed DAEM provides a global optimal solution for the parameter estimation in the mixture model and the natural color images are segmented efficiently by using the GMM with parameters estimated by morphological operations and the DAEM algorithm.
Keywords
Color Image Segmentation; Morphological Operation; Gaussian Mixture Model; Deterministic Annealing EM;
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