Paper Info
Title
Color Image Segmentation Using Fuzzy C-Regression Model
Abstract
AbstractImage segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the ground truth, unsupervised learning techniques like clustering have been largely adopted. Fuzzy clustering has been widely studied and successfully applied in image segmentation. In situations such as limited spatial resolution, poor contrast, overlapping intensities, and noise and intensity inhomogeneities, fuzzy clustering can retain much more information than the hard clustering technique. Most fuzzy clustering algorithms have originated from fuzzy c-means (FCM) and have been successfully applied in image segmentation. However, the cluster prototype of the FCM method is hyperspherical or hyperellipsoidal. FCM may not provide the accurate partition in situations where data consists of arbitrary shapes. Therefore, a Fuzzy C-Regression Model (FCRM) using spatial information has been proposed whose prototype is hyperplaned and can be either linear or nonlinear allowing for better cluster partitioning. Thus, this paper implements FCRM and applies the algorithm to color segmentation using Berkeley’s segmentation database. The results show that FCRM obtains more accurate results compared to other fuzzy clustering algorithms.
Year
DOI
Venue
2017
10.1155/2017/4582948
Periodicals
Field
DocType
Volume
Fuzzy clustering,Computer vision,Scale-space segmentation,Computer science,Segmentation,Fuzzy logic,Image processing,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Cluster analysis,Machine learning
Journal
2017
Issue
ISSN
Citations 
1
1687-7101
2
PageRank 
References 
Authors
0.41
20
2
Name
Order
Citations
PageRank
Min Chen1162.06
Simone A Ludwig21309179.41