The Study of Image Feature Extraction Based on Independent Component Analysis

Author

Xie, Ping

Author_Institution

Dept. of Comput. & Inf. Eng., HuaiNan Normal Univ., Huainan, China

fYear

2012

fDate

11-13 Aug. 2012

Firstpage

373

Lastpage

376

Abstract

This paper mainly focuses on the independent component analysis (ICA) and image recognition algorithm research, presents the supersaturated algorithm which mixed signal dimension can be smaller than the separation independent component dimension, ensure the reasonable precision. In the actual application it means that using a small number of signal acquisition device can get the character of the object, which largely reduces the processing cost, extends the application domains of ICA.

Keywords

cost reduction; feature extraction; image recognition; independent component analysis; signal detection; ICA; image feature extraction; image recognition algorithm; independent component analysis; mixed signal dimension; processing cost reduction; separation independent component dimension; signal acquisition device; supersaturated algorithm; Algorithm design and analysis; Approximation algorithms; Data mining; Educational institutions; Feature extraction; Independent component analysis; Vectors; FastICA; feature extraction; independent component analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science & Service System (CSSS), 2012 International Conference on

Conference_Location

Nanjing

Print_ISBN

978-1-4673-0721-5

Type

conf

DOI

10.1109/CSSS.2012.100

Filename

6394338