Share Email Print

Proceedings Paper

Texture classification using principle-component analysis techniques
Author(s): Xiaoou Tang; William Kenneth Stewart
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

We use a traditional principle component analysis approach, i.e. the Karhunen-Loeve Transform (KLT), to evaluate texture features in three feature spaces. The first space is the spatial space with feature vectors formed by raster scan ordering the rows of the texture image into long vectors. The second space is a transformation of the image, such as the DFT. The base of the third feature space is formed by the traditional feature vectors, whose components are the feature values extracted from commonly used algorithms, such as, spatial gray level dependence method (SGLDM), the gray level run length method (GLRLM), and the power spectral method (PSM). We apply the algorithms on sidescan sonar image classification and give a performance comparison of the three approaches.

Paper Details

Date Published: 30 December 1994
PDF: 14 pages
Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); doi: 10.1117/12.196722
Show Author Affiliations
Xiaoou Tang, Massachusetts Institute of Technology (Hong Kong)
William Kenneth Stewart, Massachusetts Institute of Technology (United States)

Published in SPIE Proceedings Vol. 2315:
Image and Signal Processing for Remote Sensing
Jacky Desachy, Editor(s)

© SPIE. Terms of Use
Back to Top