Improved adaptive principal component extraction algorithm and its application in image feature extraction - Details

Author：

Yin, Baocai (Yin, Baocai.) (Scholars：尹宝才) | Shi, Qin (Shi, Qin.) | Lei, Bin (Lei, Bin.) | Wang, Chengzhang (Wang, Chengzhang.)

Indexed by：

EI Scopus

Abstract：

An　improved　adaptive　principal　component　extraction　algorithm　is　proposed　in　this　present　study　to　overcome　the　high　algorithm　complexity　and　high　computing　complexity　that　exists　in　the　present　algorithms.　The　computing　complexity　is　decreased　by　improving　the　update　equation　of　feed-forward　network　weight　value.　Parallel　algorithm　of　the　improved　adaptive　principal　component　extraction　is　also　presented.　The　experimental　results　on　ORL　face　database　show　the　improved　adaptive　principal　component　extraction　algorithm　is　efficient　in　the　facial　feature　extraction.

Keyword：

Face recognition Neural networks Principal component analysis Database systems Parallel algorithms Feature extraction

Author Community：

[ 1 ] [Yin, Baocai]Multimedia and Intelligent Software Technology Beijing Municipal Key Laboratory, Beijing University of Technology, Beijing 100022, China
[ 2 ] [Shi, Qin]Multimedia and Intelligent Software Technology Beijing Municipal Key Laboratory, Beijing University of Technology, Beijing 100022, China
[ 3 ] [Lei, Bin]Multimedia and Intelligent Software Technology Beijing Municipal Key Laboratory, Beijing University of Technology, Beijing 100022, China
[ 4 ] [Wang, Chengzhang]Multimedia and Intelligent Software Technology Beijing Municipal Key Laboratory, Beijing University of Technology, Beijing 100022, China

Reprint Author's Address：

Email：

ybc@bjut.edu.cn

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Source ：

Journal of Computational Information Systems

ISSN： 1553-9105

Year： 2005

Issue： 2

Volume： 1

Page： 253-257

Cited Count：

WoS CC Cited Count：

SCOPUS Cited Count：

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 2

Affiliated Colleges：

建筑与城市规划学院本学院/部未明确归属的数据

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