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