DocumentCode :
3307604
Title :
A New Method for Image Segmentation Based on Integration Technique
Author :
Luo, Hui-lan ; Wei Wang ; Jing Li
Author_Institution :
Sch. of Inf. Eng., Jiangxi Univ. of Sci. & Technol., Ganzhou, China
fYear :
2010
fDate :
24-25 April 2010
Firstpage :
342
Lastpage :
345
Abstract :
There are some general procedures for image segmentation. One can apply different algorithms to create different clusterings of the data. Some clustering algorithms like k-means require initialization of parameters. Different initialization can lead to different data clusterings. In this paper, we explore the idea of evidence accumulation for combining the result of multiple clustering. Initially, data is decomposed into a large number of compact clusters; the K-means algorithm performs this decomposition, with several clusterings obtained by N random initializations of K-means. Taking the co-occurrences of pairs of patterns in the same cluster as means for their association, we deal with the image with the means. We compare our new method with the k-means. The experiment shows that our approach can achieve higher or comparable performance than the old method.
Keywords :
Clustering algorithms; Computer vision; Histograms; Image edge detection; Image segmentation; Level set; Machine vision; Man machine systems; Partitioning algorithms; Pixel; K-means; image segmentation; integration technique;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Vision and Human-Machine Interface (MVHI), 2010 International Conference on
Conference_Location :
Kaifeng, China
Print_ISBN :
978-1-4244-6595-8
Electronic_ISBN :
978-1-4244-6596-5
Type :
conf
DOI :
10.1109/MVHI.2010.174
Filename :
5532723
Link To Document :
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