Linear Transformation Technology for Image Feature Drop Dimension

Author

Xiao, Feng ; Zhou, Mingyuan ; Geng, Guohua

Author_Institution

Sch. of Inf. Sci. & Technol., Northwest Univ., Xi´´an, China

fYear

2011

fDate

8-9 Oct. 2011

Firstpage

331

Lastpage

333

Abstract

Linear Transformation Technology can eliminate component relevance of image high dimension feature vectors and drop dimension of feature vectors, then extract image features effectively. This paper analyses and discusses the methods of PCA (Prinapal Component Analysis), ICA (Independent Component Analysis), and SVD (Singular Value Decomposition) based On Linear Transformation Technology. The methods of PCA and SVD can eliminate 2-order relevance between feature vectors and ICA can eliminate high-order relevance between inputed feature vectors.

Keywords

feature extraction; independent component analysis; principal component analysis; singular value decomposition; ICA; PCA; SVD; image feature drop dimension; image feature extraction; image high dimension feature vector; independent component analysis; linear transformation technology; principal component analysis; singular value decomposition; Data mining; Educational institutions; Feature extraction; Image coding; Principal component analysis; Semantics; Vectors; independent component analysis; linear transformation; prinapal component analysis; singular value decomposition;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge Acquisition and Modeling (KAM), 2011 Fourth International Symposium on

Conference_Location

Sanya

Print_ISBN

978-1-4577-1788-8

Type

conf

DOI

10.1109/KAM.2011.95

Filename

6137649