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A New Modified Gaussian Mixture Model for Color-Texture Segmentation

Author(s): M. Sujaritha | S. Annadurai

Journal: Journal of Computer Science
ISSN 1549-3636

Volume: 7;
Issue: 2;
Start page: 279;
Date: 2011;
Original page

Keywords: Gaussian mixture model | Expectation Maximization (EM) | Bayesian pixel classification | color texture segmentation | MAP estimation | EM algorithm | Maximum A Posteriori (MAP) | Spatially Variant Finite Mixture Model (SVFMM) | Markov Random Field (MRF) | Probabilistic Rand Index (PRI) | Boundary Displacement Errors (BDE) | Variation of Information (VoI)

Problem statement: This study presents a new, simple and efficient modified Gaussian mixture model based clustering algorithm for color-texture segmentation. The proposed mixture model introduces a new component density function which incorporates spatial information and the weighting factor for neighborhood effect is fully adaptive to the image content. Approach: It enhances the smoothness towards piecewise-homogeneous segmentation and reduces the edge-blurring effect. An Expectation Maximization (EM) model fitting Maximum A Posteriori (MAP) algorithm segments the image by utilizing the pixelÂ’s color and texture features and the captured neighborhood relationships among them. Results: The algorithm simultaneously calculates the model parameters and segments the pixels iteratively in an interleaved manner. Finally, it converges to a solution where the model parameters and pixel labels are stabilized within a specified criterion. Conclusion:The experimental results with synthetic and natural images demonstrate that the proposed method is effective in improving the segmentation and it outperforms the Fusion of Clustering Results (FCR) algorithm, which is the recent popular segmentation approach.

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