1. A Color Texture Image Segmentation Method Based on Fuzzy c-Means Clustering and Region-Level Markov Random Field Model
- Author
-
Pengwei Li, Guoying Liu, and Yun Zhang
- Subjects
Fuzzy clustering ,Markov random field ,Article Subject ,Pixel ,business.industry ,lcsh:Mathematics ,General Mathematics ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,General Engineering ,Pattern recognition ,Image segmentation ,lcsh:QA1-939 ,Fuzzy logic ,Image texture ,lcsh:TA1-2040 ,Computer Science::Computer Vision and Pattern Recognition ,Computer vision ,Artificial intelligence ,lcsh:Engineering (General). Civil engineering (General) ,Scale (map) ,Cluster analysis ,business ,Mathematics - Abstract
This paper presents a variation of the fuzzy local information c-means clustering (FLICM) algorithm that provides color texture image clustering. The proposed algorithm incorporates region-level spatial, spectral, and structural information in a novel fuzzy way. The new algorithm, called RFLICM, combines FLICM and region-level Markov random field model (RMRF) together to make use of large scale interactions between image patches instead of pixels. RFLICM can overcome the weakness of FLICM when dealing with textured images and at the same time enhances the clustering performance. The major characteristic of RFLICM is the use of a region-level fuzzy factor, aiming to guarantee texture homogeneity and preserve region boundaries. Experiments performed on synthetic and remote sensing images show that RFLICM is effective in providing accuracy to color texture images.
- Published
- 2015