Paper Info
Title
An improved approach for image segmentation based on color and local homogeneity features
Abstract
In this paper, we propose an improved approach for image segmentation based on color and local homogeneity features. A given image is transformed into a quantized image by a self-constructing fuzzy clustering. Then, a color-based region image and an initial seeded region image are obtained from the quantized image by color-based and homogeneity-based region growing methods, respectively. After that, we combine these two images to generate a refined seeded region image and obtain an initial segmented image by a region-based region growing. Finally, merging based on color similarities and sizes of regions is performed for avoiding the problem of over-segmentation. Compared with the other method, experimental results show that the segmented regions obtained by our approach are more reasonable and precise.
Year
DOI
Venue
2009
10.1109/ICASSP.2009.4959811
ICASSP
Keywords
Field
DocType
quantized image,region-based region,improved approach,color similarity,local homogeneity feature,homogeneity-based region,color-based region image,segmented region,initial segmented image,region image,image segmentation,pixel,quantization,color quantization,fuzzy set theory,information retrieval,pattern recognition,application software,mathematical model,computer vision,region growing,image retrieval,merging,data mining,fuzzy clustering
Computer vision,Image gradient,Color histogram,Feature detection (computer vision),Pattern recognition,Computer science,Image texture,Binary image,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Region growing
Conference
ISSN
Citations 
PageRank 
1520-6149
1
0.38
References 
Authors
5
4
Name
Order
Citations
PageRank
Chen-Sen Ouyang115717.15
Chia-Te Chou2303.27
Ci-Fong Jhan331.15
Jhih-Yong Huang410.72