Title
Graph Segmentation Revisited: Detailed Analysis And Density Learning Based Implementation
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.
Year
DOI
Venue
2010
10.1109/ICME.2010.5583553
2010 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME 2010)
Keywords
Field
DocType
SST, RSST, mean shift, segmentation
Graph theory,Density estimation,Kernel (linear algebra),Pattern recognition,Segmentation,Computer science,Image segmentation,Robustness (computer science),Artificial intelligence,Spanning tree,Mean-shift
Conference
ISSN
Citations 
PageRank 
1945-7871
0
0.34
References 
Authors
4
6
Name
Order
Citations
PageRank
Zhiding Yu142130.08
Oscar C. Au21592176.54
Ketan Tang310612.98
Jiali Li4499.29
Lingfeng Xu5539.81
Xingyu Zhang6307.01