Paper Info
Title
The kernel orthogonal mutual subspace method and its application to 3D object recognition
Abstract
This paper proposes the kernel orthogonal mutual subspace method (KOMSM) for 3D object recognition. KOMSM is a kernel-based method for classifying sets of patterns such as video frames or multiview images. It classifies objects based on the canonical angles between the nonlinear subspaces, which are generated from the image patterns of each object class by kernel PCA. This methodology has been introduced in the kernel mutual subspace method (KMSM). However, KOMSM is different from KMSM in that nonlinear class subspaces are orthogonalized based on the framework proposed by Fukunaga and Koontz before calculating the canonical angles. This orthogonalization provides a powerful feature extraction method for improving the performance of KMSM. The validity of KOMSM is demonstrated through experiments using face images and images from a public database.
Year
DOI
Venue
2007
10.1007/978-3-540-76390-1_46
ACCV
Keywords
Field
DocType
object recognition,kernel orthogonal mutual subspace,kernel pca,canonical angle,powerful feature extraction method,kernel-based method,nonlinear class subspaces,kernel mutual subspace method,object class,nonlinear subspaces,feature extraction
Kernel (linear algebra),Computer vision,Radial basis function kernel,Subspace topology,Pattern recognition,Kernel embedding of distributions,Computer science,Feature extraction,Linear subspace,Kernel principal component analysis,Artificial intelligence,Orthogonalization
Conference
Volume
ISSN
ISBN
4844
0302-9743
3-540-76389-9
Citations 
PageRank 
References 
35
1.33
7
Authors
2
Name
Order
Citations
PageRank
Kazuhiro Fukui182871.55
Osamu Yamaguchi267144.09