Paper Info
Title
Glocal shape context descriptor in cluttered images
Abstract
Shape context has been proven to be an effective method for both local feature matching and global context description. In this paper, we propose a method to build a glocal shape context descriptor in cluttered images. By using the proposed keypoint centered multiple scale edge detection (KMSED) method, glocal shape context encodes fine-scale edges in the keypoint center region while coarse-scale edges in the outer region. In this way, local and global image information are encoded at the same time into a 68 dimension feature vector. Experiments show that the proposed glocal shape context makes significant enhancement over the local shape context descriptor and outperforms SIFT under severe illumination change and high JPEG compression.
Year
Venue
Keywords
2012
ICPR
fine-scale edges,image coding,image matching,local image information,cluttered images,edge detection,kmsed method,keypoint centered multiple scale edge detection method,global image information,glocal shape context descriptor,jpeg compression,coarse-scale edges,local feature matching,keypoint center region
Field
DocType
ISSN
Scale-invariant feature transform,Computer vision,Feature vector,Pattern recognition,Edge detection,Image matching,Computer science,Effective method,Feature matching,Artificial intelligence,Jpeg compression,Shape context
Conference
1051-4651
ISBN
Citations 
PageRank 
978-1-4673-2216-4
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Shimiao Li1235.48
Wei Xiong2236.75
Tan Dat Nguyen300.34