Abstract
Gaussian Mixture Model (GMM) has been widely applied in image segmentation. However, the pixels themselves are considered independent of each other, making the segmentation result sensitive to noise. To overcome this problem for the segmentation process we propose a mixture model useing Markov Random Filed (MRF) that aims to incorporate spatial relationship among neighborhood pixels into the GMM. The proposed model has a simplified structure that allows the Expectation Maximization (EM) algorithm to be directly applied to the log-likelihood function to compute the optimum parameters of the mixture model. The experimental results show that our method has more advantage in image segmentation than other methods in terms of accuracy and quality of segmented image, and simple performance.
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Tran, K.A., Vo, N.Q., Nguyen, T.T., Lee, G. (2014). Gaussian Mixture Model Based on Hidden Markov Random Field for Color Image Segmentation. In: Jeong, YS., Park, YH., Hsu, CH., Park, J. (eds) Ubiquitous Information Technologies and Applications. Lecture Notes in Electrical Engineering, vol 280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41671-2_25
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DOI: https://doi.org/10.1007/978-3-642-41671-2_25
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