Compact feature vector for natural texture classification

Citation

• Naghdy, G. A., Tanbe, M., & Ogunbona, P. O. (1995). Compact feature vector for natural texture classification. In ANZIIS 1995 - Proceedings of the 3rd Australian and New Zealand Conference on Intelligent Information Systems (pp. 59-63). doi:10.1109/ANZIIS.1995.705715

Digital Object Identifier (doi)

• 10.1109/ANZIIS.1995.705715

Scopus Eid

• 2-s2.0-85063529185

Web Of Science Accession Number

Start Page

• 59

End Page

• 63

Volume

Issue

Place Of Publication

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 experiment. The results obtained represent a high accuracy of classification for those textures with pronounced orientation. Classification accuracy up to 90% is obtained using 50-fealure vector. A lower classification accuracy is obtained with a 20-feature vector (about 70%), due to the fact that the orientation-space 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.

UOW Authors

•   Ogunbona, Philip

Publication Date

• 1995

Publisher

• IEEE 

Citation

• Naghdy, G. A., Tanbe, M., & Ogunbona, P. O. (1995). Compact feature vector for natural texture classification. In ANZIIS 1995 - Proceedings of the 3rd Australian and New Zealand Conference on Intelligent Information Systems (pp. 59-63). doi:10.1109/ANZIIS.1995.705715

Digital Object Identifier (doi)

• 10.1109/ANZIIS.1995.705715

Scopus Eid

• 2-s2.0-85063529185

Web Of Science Accession Number

Start Page

• 59

End Page

• 63

Volume

Issue

Place Of Publication