DocumentCode :
2294167
Title :
Graph segmentation revisited: Detailed analysis and density learning based implementation
Author :
Yu, Zhiding ; Au, Oscar C. ; Tang, Ketan ; Li, Jiali ; Xu, Lingfeng ; Zhang, Xingyu
Author_Institution :
Dept. of Electron. & Comput. Eng., Hong Kong Univ. of Sci. & Technol., Kowloon, China
fYear :
2010
fDate :
19-23 July 2010
Firstpage :
602
Lastpage :
607
Abstract :
In this paper we give a step-by-step detailed analysis on the performance of shortest spanning tree (SST) and its revised version, recursive SST (RSST). We further propose a novel segmentation scheme based on recursive SST in the warped domain produced by density estimation. The proposed method is robust for variant natural image input and is easy to implement. Experimental results and comparisons with other methods have illustrated the effectiveness and robustness of the proposed method.
Keywords :
graph theory; image segmentation; recursive estimation; trees (mathematics); density learning based implementation; graph segmentation; recursive shortest spanning tree; variant natural image input; warped domain; Construction industry; Estimation; Image edge detection; Image segmentation; Kernel; Pixel; Robustness; RSST; SST; mean shift; segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia and Expo (ICME), 2010 IEEE International Conference on
Conference_Location :
Suntec City
ISSN :
1945-7871
Print_ISBN :
978-1-4244-7491-2
Type :
conf
DOI :
10.1109/ICME.2010.5583553
Filename :
5583553
Link To Document :
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