Title
Global and local preserving feature extraction for image categorization
Abstract
In this paper, we describe a feature extraction method: Global and Local Preserving Projection (GLPP). GLPP is based on PCA and the recently proposed Locality Preserving Projection (LPP) method. LPP can preserve local information, while GLPP can preserve both global and local information. In this paper we investigate the potential of using GLPP for image categorization. More specifically, we experiment on palmprint images. Palmprint image has been attracting more and more attentions in the image categorization/recognition area in recent years. Experiment is based on benchmark dataset PolyU, using Error Rate as performance measure. Comparison with LPP and traditional algorithms show that GLPP is promising.
Year
DOI
Venue
2007
10.1007/978-3-540-74695-9_56
ICANN (2)
Keywords
Field
DocType
image categorization,local preserving projection,local information,benchmark dataset,locality preserving projection,palmprint image,feature extraction method,performance measure,recent year,error rate,feature extraction
Categorization,Locality,Pattern recognition,Computer science,Word error rate,Feature extraction,Independent component analysis,Artificial intelligence,Machine learning
Conference
Volume
ISSN
ISBN
4669
0302-9743
3-540-74693-5
Citations 
PageRank 
References 
1
0.37
19
Authors
6
Name
Order
Citations
PageRank
Rongfang Bie154768.23
Xin Jin233362.83
Chuan Xu314022.01
Chuanliang Chen4526.49
Anbang Xu535130.52
Xian Shen6142.51