Global and local preserving feature extraction for image categorization

Authors:
Rongfang Bie;Xin Jin;Chuan Xu;Chuanliang Chen;Anbang Xu;Xian Shen
Affiliations:
College of Information Science and Technology, Beijing Normal University, Beijing, P.R. China;College of Information Science and Technology, Beijing Normal University, Beijing, P.R. China;College of Information Science and Technology, Beijing Normal University, Beijing, P.R. China;College of Information Science and Technology, Beijing Normal University, Beijing, P.R. China;College of Information Science and Technology, Beijing Normal University, Beijing, P.R. China;College of Information Science and Technology, Beijing Normal University, Beijing, P.R. China
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

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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.