DocumentCode
3494433
Title
Minimum Spanning Tree and Color Image Segmentation
Author
Zhang, Xue-xi ; Yang, Yi-Min
Author_Institution
Guangdong Univ. of Technol., Guangzhou
fYear
2008
fDate
6-8 April 2008
Firstpage
900
Lastpage
904
Abstract
Image segmentation based on graph theory is mainly used for gray image now, and thresholding of segmentation should be predefined. Combining with maximum between -and- within -class in statistics theory, this paper suggests an unsupervised method for color image segmentation. The image is mapped into an weighted undirected graph, the pixels are considered as nodes, and minimum spanning tree is constructed by Kruskal algorithm .The best thresholding is obtained by maximum objective function to realize unsupervised segmentation. Experiment results show that the new algorithm ensures the color image segmentation excellent disturbance attenuation performance and better separability.
Keywords
image colour analysis; image segmentation; statistical analysis; trees (mathematics); Kruskal algorithm; color image segmentation; graph theory; gray image; image thresholding; maximum objective function; minimum spanning tree; statistics theory; weighted undirected graph; Automation; Clustering algorithms; Clustering methods; Color; Educational institutions; Graph theory; Image segmentation; Pixel; Statistics; Tree graphs;
fLanguage
English
Publisher
ieee
Conference_Titel
Networking, Sensing and Control, 2008. ICNSC 2008. IEEE International Conference on
Conference_Location
Sanya
Print_ISBN
978-1-4244-1685-1
Electronic_ISBN
978-1-4244-1686-8
Type
conf
DOI
10.1109/ICNSC.2008.4525344
Filename
4525344
Link To Document