Compact feature vector for natural texture classification

Author

Nagheky, G.A. ; Taube, M. ; Ogunbona, P.O.

Author_Institution

Dept. of Electr. & Comput. Eng., Wollongong Univ., NSW, Australia

fYear

1995

fDate

35030

Firstpage

59

Lastpage

63

Abstract

Gabor filter masks are used to extract succinct feature vectors from natural and synthetic textures. Textures from the Brodatz collection and real images, captured through a CCD camera, are used in the reported experiment. The results obtained represent a high accuracy of classification for those textures with pronounced orientation. A classification accuracy of up to 90% is obtained using a 50-feature vector. A lower classification accuracy is obtained with a 20-feature vector (about 70%), due to the fact that the orientation is not finely sampled so as to capture all the possibilities. These results compare well with results obtained using the same method on computer-generated artificial textures

Keywords

feature extraction; filtering theory; image classification; image texture; masks; spatial filters; vectors; Brodatz collection; CCD camera; Gabor filter masks; classification accuracy; compact feature vector; computer-generated artificial textures; natural texture classification; texture orientation; Bandwidth; Computer vision; Detectors; Filtering; Frequency domain analysis; Gabor filters; Humans; Mathematical model; Surface texture; Visual system;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Systems, 1995. ANZIIS-95. Proceedings of the Third Australian and New Zealand Conference on

Conference_Location

Perth, WA

Print_ISBN

0-86422-430-3

Type

conf

DOI

10.1109/ANZIIS.1995.705715

Filename

705715