Automatic Fuzzy Clustering Framework 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-...

Fuzzy Neighbor Voting for Automatic Image Annotation

With quick development of digital images and the availability of imaging tools, massive amounts of images are created. Therefore, efficient management and suitable retrieval, especially by computers, is one of themost challenging fields in image processing. Automatic image annotation (AIA) or refers to attaching words, keywords or comments to an image or to a selected part of it. In this paper,...

K - Means Clustering for Automatic Image Segmentation

A clustering fuzzy approach for image segmentation

Segmentation is a fundamental step in image description or classi1cation. In recent years, several computational models have been used to implement segmentation methods but without establishing a single analytic solution. However, the intrinsic properties of neural networks make them an interesting approach, despite some measure of ine5ciency. This paper presents a clustering approach for image...

Automatic Fuzzy Algorithms for Reliable Image Segmentation

The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. In particular, medical image segmentation is complex, and automatically detecting regions or clusters of such widely varying sizes is a challenging task. In this paper, we present automatic fuzzy k-means, and kernelized fuzzy c-means algorithms by conside...