Image segmentation is used as the preliminary step in many of the image processing applications. Some of the applications depends heavily on the initial models obtained as silhouettes. Segmentation Result should be _nite to the _nest extension possible to get better result out of the succeeding operations. Making perfect initial model silhouette is a problem and challenge. Multiview segmentation is a relatively new area of segmentation which can be effectively used for the purpose of 3D modeling, Animation, Object recognition, Multimedia search etc. Out of different ways of segmentation, study reveals that Bayesian method is the most suitable type for silhouette estimation because of the nature of utilizing previous details. The proposed method utilizes Bayesian method along with Graph cut method for the silhouette optimization. The Normalized graph cut overcomes the limitations of ordinary graph cut and provides advantages like noise removal, reduced false alarm rate etc. Here the proposal is an automatic way (does not need user interaction, Background knowledge) for multiview segmentation Which combines probabilistic method along with normalized Graph cut optimization to provide Reduced false alarm rate(FAR) and better silhouette for the foreground to be extracted.

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Multiview segmentation Segmentation Automatic segmentation Bayesian segmentation Normalized graph cut segmentation