Nonparametric clustering for image segmentation

Image Segmentation: Type–2 Fuzzy Possibilistic C-Mean Clustering Approach

Image segmentation is an essential issue in image description and classification. Currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. Moreover, there are many uncertainties and vagueness in images, which crisp clustering and even Type-1 fuzzy clustering could not handle. Hence, Type-...

Contextual clustering for image segmentation

Giuseppe Satalino IESI-CNR Via Amendola 166/5 70126 Bari, Italy E-mail: [email protected] Abstract. The unsupervised Pappas adaptive clustering (PAC) algorithm is a well-known Bayesian and contextual procedure for pixel labeling. It applies only to piecewise constant or slowly varying intensity images that may be corrupted by an additive white Gaussian noise field independent of the scene...

Finding Salient Regions in Images: Nonparametric Clustering for Image Segmentation and Grouping

A major problem in Content-Based Image Retrieval (CBIR) is the unsupervised iden-tiication of perceptually salient regions in images. We contend that this problem can be tackled by mapping the pixels into various feature-spaces, whereupon they are subjected to a grouping-algorithm. In this paper we develop a robust and versatile non-parametric clustering algorithm that is able to handle the unb...

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

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

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 1 / 4