The texture classification using the fusion of decisions from different texture classifiers

Author

Pharsook, S. ; Kasetkasem, T. ; Larmsrichan, P. ; Siddhichai, S. ; Chanwimaluang, T. ; Isshiki, T.

Author_Institution

Dept. of Electr. Eng., Kasetsart Univ., Bangkok, Thailand

fYear

2011

fDate

17-19 May 2011

Firstpage

1003

Lastpage

1006

Abstract

The improved of texture classification accuracy by using the probability weighted combination method of three texture features extraction consist of thE0020 Gray-Level Co-occurrence Matrix (GLCM), Semivariogram Function and Gaussian Markov Random Fields (GMRFs). Five different textures images are used in the experiment. The classifier that use for classify the extracted features in this research is Support Vector Machines (SVMs). The experimental result shows that the average accuracy of the combination method with probability weight up to 95.71%, which is better than the simple combination method about 2%.

Keywords

Gaussian processes; Markov processes; feature extraction; image classification; image texture; support vector machines; GLCM; Gaussian Markov random field; Semivariogram function; decision fusion; different texture classifiers; gray level cooccurrence matrix; probability weighted combination method; support vector machine; texture classification; texture features extraction; textured image; Arrays; Entropy; Markov processes; Sensors; Tutorials; Combination; Gaussian Markov Random Fields (GMRFs); Gray-Level Co-occurrence Matrix (GLCM); Semivarigram; Texture classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2011 8th International Conference on

Conference_Location

Khon Kaen

Print_ISBN

978-1-4577-0425-3

Type

conf

DOI

10.1109/ECTICON.2011.5948012

Filename

5948012