Research Article

Bayesian Framework for image segmentation Based on Nonparametric Clustering with Spatial Neighborhood Information

by  Kirati Imëne, Tlili Yamina
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 35 - Issue 12
Published: December 2011
Authors: Kirati Imëne, Tlili Yamina
10.5120/4540-6445
PDF

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 

                        @article{ 10.5120/4540-6445,
                        author  = { Kirati Imëne,Tlili Yamina },
                        title   = { Bayesian Framework for image segmentation Based on Nonparametric Clustering with Spatial Neighborhood Information },
                        journal = { International Journal of Computer Applications },
                        year    = { 2011 },
                        volume  = { 35 },
                        number  = { 12 },
                        pages   = { 21-24 },
                        doi     = { 10.5120/4540-6445 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2011
                        %A Kirati Imëne
                        %A Tlili Yamina
                        %T Bayesian Framework for image segmentation Based on Nonparametric Clustering with Spatial Neighborhood Information%T 
                        %J International Journal of Computer Applications
                        %V 35
                        %N 12
                        %P 21-24
                        %R 10.5120/4540-6445
                        %I Foundation of Computer Science (FCS), NY, USA
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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Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Image segmentation Nonparametric clustering Spatial Information

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