DocumentCode :
2963352
Title :
SNN clustering kernel technique for content-based scene matching
Author :
Wang, Zhong ; Hao, Yanling ; Xiong, Zhilan ; Sun, Feng
Author_Institution :
Harbin Eng. Univ., Harbin
fYear :
2008
fDate :
9-10 Sept. 2008
Firstpage :
1
Lastpage :
6
Abstract :
In this paper, the radial basis vector (RBV) is proposed to describe the descriptor set of an image. And the shared nearest neighbor clustering kernel (SNNCK) technique is proposed to match RBV pairs. SNNCK is based on the charge attractive model, which will make the unequal-dimensional data sets clustering naturally. Thus, this novel algorithm is able to match the unequal-dimensional data sets when the number of descriptors of two images are unequal. It also can automatically extract the repetition pattern of the reference date set, which is helpful to avoid the wrong matching. Experimental results are also provided, and these results demonstrate superior performances of SNNCK algorithm by using the feature point sets with strong disturbs.
Keywords :
feature extraction; image matching; pattern clustering; content-based scene matching; pattern extraction; radial basis vector; shared nearest neighbor clustering kernel technique; Clustering algorithms; Computer vision; Context modeling; Kernel; Layout; Nearest neighbor searches; Pattern matching; Robustness; Shape measurement; Stability; (RBV); Content-based model; Radial basis vector; Scene matching; Shared nearest neighbor clustering kernel (SNNCK);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cybernetic Intelligent Systems, 2008. CIS 2008. 7th IEEE International Conference on
Conference_Location :
London
Print_ISBN :
978-1-4244-2914-1
Electronic_ISBN :
978-1-4244-2915-8
Type :
conf
DOI :
10.1109/UKRICIS.2008.4798972
Filename :
4798972
Link To Document :
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