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Bayesian Framework for image segmentation Based on Nonparametric Clustering with Spatial Neighborhood Information
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 35 - Number 12 |
| Year of Publication: 2011 |
| Authors: Kirati ImËne, Tlili Yamina |
10.5120/4540-6445
|
Kirati ImËne, Tlili Yamina . Bayesian Framework for image segmentation Based on Nonparametric Clustering with Spatial Neighborhood Information. International Journal of Computer Applications. 35, 12 ( December 2011), 21-24. DOI=10.5120/4540-6445
Abstract
In this paper, we present a Bayesian framework for image segmentation based upon spatial nonparametric clustering. To estimate the density function on a nonparametric form, the proposed model exploits local Gaussian kernels. In addition, we have incorporated the spatial information to the clustering process by adding a spatial function for weighting the posterior probabilities. The main advantages of this model are two. First due to the non parametric structure, it does not require the image regions to have a particular type of density distribution. Second, adding spatial information yields more homogenous and smoothed regions. The experimental results based on real images demonstrate the efficiency of the proposed method and indicate clearly its robustness to noise.
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