We present an unsupervised segmentation algorithm combining the mean shift procedure and deterministic annealing expectation maximization (DAEM) called MS-DAEM algorithm. We use the mean shift procedure to determine the number of components in a mixture model and to detect their modes of each mixture component. Next, we have adopted the Gaussian mixture model (GMM) to represent the probability distribution of color feature vectors. A DAEM formula is used to estimate the parameters of the GMM which represents the multi-colored objects statistically. The experimental results show that the mean shift part of the proposed MS-DAEM algorithm is efficient to determine the number of components and initial modes of each component in mixture models. And also it shows that the DAEM part provides a global optimal solution for the parameter estimation in a mixture model and the natural color images are segmented efficiently by using the GMM with components estimated by MS-DAEM algorithm. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Cho, W., Park, J., Lee, M., & Park, S. (2005). Unsupervised color image segmentation using mean shift and deterministic annealing EM. In Lecture Notes in Computer Science (Vol. 3483, pp. 867–876). Springer Verlag. https://doi.org/10.1007/11424925_91
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