Paper Info
Title
Shape retrieval by principal components descriptor
Abstract
Shape information is an important distribution to Content-Base Image Retrieval (CBIR) systems. There are two major types of shape descriptors, namely region-based and contour-based. In this paper we present a shape retrieval method that makes use of a contour-based descriptor, Principal Components Descriptor (PCD). In PCD, shapes are aligned on principal axes and described by a combination of the mean shape and weighted eigenvectors. The retrieval is achieved by comparing the weights of the eigenvectors. The developed approach is applied to Sharvit's Silhouettes database and the results are compared with MPEG-7 standard contour-based descriptor, Curvature Scale Space (CSS). The comparison indicates that PCD shows higher accuracy than CSS.
Year
DOI
Venue
2005
10.1007/11552499_69
ICAPR (2)
Keywords
Field
DocType
shape information,weighted eigenvectors,shape descriptors,principal components descriptor,curvature scale space,shape retrieval method,mean shape,content-base image,mpeg-7 standard contour-based descriptor,contour-based descriptor,eigenvectors,principal component
Computer vision,Feature vector,Curvature,GLOH,Pattern recognition,Computer science,Principal axis theorem,Image retrieval,Scale space,Artificial intelligence,Eigenvalues and eigenvectors,Principal component analysis
Conference
Volume
ISSN
ISBN
3687
0302-9743
3-540-28833-3
Citations 
PageRank 
References 
0
0.34
11
Authors
3
Name
Order
Citations
PageRank
Binhai Wang142.55
Andrew J. Bangham26513.15
Yanong Zhu3685.58