Paper Info
Title
A fuzzy clustering image segmentation algorithm based on Hidden Markov Random Field models and Voronoi Tessellation.
Abstract
Using VT and HMRF model to improve robustness and noise insensitiveness. Combing VT-HMRF model into the FCM based framework. Comparing results with traditional FCM based algorithms. In this paper, we present new results related to the Voronoi Tessellation (VT) and Hidden Markov Random Field (HMRF) based Fuzzy C-Means (FCM) algorithm (VTHMRF-FCM) for texture image segmentation. In the VTHMRF-FCM algorithm, a VTHMRF model is established by using VT to partition an image domain into sub-regions (Voronoi polygons) and HMRF to describe the relationship of neighbor sub-regions. Based on the VTHMRF model, the objective function of VTHMRF-FCM is defined by adding a regularization term of Kullback-Leibler (KL) divergence information to FCM objective function. The proposed algorithm combines the benefits stemming from robust regional HMRF and FCM based clustering segmentation. Segmentation experiments on synthetic and real images by the proposed and other improved FCM algorithms are performed. Their results demonstrate that the proposed algorithm can obtain much better segmentation results than other FCM based methods.
Year
DOI
Venue
2017
10.1016/j.patrec.2016.11.019
Pattern Recognition Letters
Keywords
Field
DocType
Voronoi Tessellation (VT),Hidden Markov Random Model (HMRF),Fuzzy clustering,Image segmentation
Fuzzy clustering,Pattern recognition,Hidden Markov random field,Segmentation,Robustness (computer science),Image segmentation,Artificial intelligence,Voronoi diagram,Real image,Cluster analysis,Mathematics
Journal
Volume
Issue
ISSN
85
C
0167-8655
Citations 
PageRank 
References 
6
0.44
14
Authors
4
Name
Order
Citations
PageRank
Quanhua Zhao181.84
XiaoLi Li261.12
Yu Li3156.13
Xuemei Zhao411111.89