Title :
Minimum Spanning Tree and Color Image Segmentation
Author :
Zhang, Xue-xi ; Yang, Yi-Min
Author_Institution :
Guangdong Univ. of Technol., Guangzhou
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;
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
DOI :
10.1109/ICNSC.2008.4525344