Back to Search
Start Over
A Color Texture Image Segmentation Method Based on Fuzzy c-Means Clustering and Region-Level Markov Random Field Model
- Source :
- Mathematical Problems in Engineering, Vol 2015 (2015)
- Publication Year :
- 2015
- Publisher :
- Hindawi Limited, 2015.
-
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.
- 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
Subjects
Details
- ISSN :
- 15635147 and 1024123X
- Volume :
- 2015
- Database :
- OpenAIRE
- Journal :
- Mathematical Problems in Engineering
- Accession number :
- edsair.doi.dedup.....e0d98f530e08c70de5d010bf18015f0e