A maximum likelihood approach to texture classification using wavelet transform

Author

Thyagarajan, K.S. ; Nguyen, Tom ; Persons, Charles E.

Author_Institution

Dept. of Electr. & Comput. Eng., San Diego State Univ., CA, USA

Volume

2

fYear

1994

fDate

13-16 Nov 1994

Firstpage

640

Abstract

The paper describes a method of classifying natural textures based on maximum likelihood parameter estimation technique. The wavelet transform (WT) is used to represent the textural images in multiresolution. Co-occurrence matrices are then computed for the different scales of the wavelet transform and textural features are obtained from the co-occurrence matrices. Then a maximum likelihood classifier is designed using a set of training texture samples. Ten different Brodot textures have been classified using this procedure with an average classification accuracy of 99.7

Keywords

image classification; image resolution; image texture; matrix algebra; maximum likelihood estimation; wavelet transforms; Brodot textures; classification accuracy; co-occurrence matrices; maximum likelihood approach; maximum likelihood classifier; maximum likelihood parameter estimation technique; multiresolution; texture classification; training texture samples; wavelet transform; Biomedical imaging; Electronic mail; Entropy; Image analysis; Image resolution; Image texture analysis; Layout; Satellites; Spatial resolution; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference

Conference_Location

Austin, TX

Print_ISBN

0-8186-6952-7

Type

conf

DOI

10.1109/ICIP.1994.413649

Filename

413649