Rotation-Invariant Features for Texture Image Classification

Author

Jalil, Abdul ; Qureshi, Ijaz Mansoor ; Manzar, A. ; Zahoor, R.A. ; Jinnah, M.A.

Author_Institution

Islamabad Univ.

fYear

0

fDate

0-0 0

Firstpage

1

Lastpage

4

Abstract

Texture features based on wavelet transform are sensitive to texture rotation and translation. This paper develops a new rotation invariant texture analysis technique using principal components analysis (PCA) and wavelet transform. The PCA is first used to calculate the angle of the principal direction of the texture. Then, the texture is rotated in the opposite direction by the same angle as detected by PCA. Finally a wavelet transform is applied to the preprocessed texture to extract features which are rotation invariant

Keywords

feature extraction; image classification; image texture; principal component analysis; wavelet transforms; feature extraction; principal components analysis; rotation invariant texture analysis; texture image classification; wavelet transform; Eigenvalues and eigenfunctions; Feature extraction; Image analysis; Image classification; Image texture analysis; Karhunen-Loeve transforms; Principal component analysis; Wavelet analysis; Wavelet packets; Wavelet transforms; Principal Component Analysis; Rotation invariant; Texture analysis; Wavelet Transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering of Intelligent Systems, 2006 IEEE International Conference on

Conference_Location

Islamabad

Print_ISBN

1-4244-0456-8

Type

conf

DOI

10.1109/ICEIS.2006.1703136

Filename

1703136