Texture information in run-length matrices

Author

Tang, Xiaoou

Author_Institution

Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong

Volume

7

Issue

11

fYear

1998

fDate

11/1/1998 12:00:00 AM

Firstpage

1602

Lastpage

1609

Abstract

We use a multilevel dominant eigenvector estimation algorithm to develop a new run-length texture feature extraction algorithm that preserves much of the texture information in run-length matrices and significantly improves image classification accuracy over traditional run-length techniques. The advantage of this approach is demonstrated experimentally by the classification of two texture data sets. Comparisons with other methods demonstrate that the run-length matrices contain great discriminatory information and that a good method of extracting such information is of paramount importance to successful classification

Keywords

eigenvalues and eigenfunctions; feature extraction; image classification; image texture; matrix algebra; image classification accuracy; multilevel dominant eigenvector estimation algorithm; run-length matrices; run-length texture feature extraction; texture data sets; texture information; Data mining; Feature extraction; Image classification; Image processing; Image texture analysis; Pattern analysis; Pattern classification; Pattern recognition; Pixel; Surface texture;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/83.725367

Filename

725367