Gabor Filters as Feature Images for Covariance Matrix on Texture Classification Problem

Authors:
Jing Yi Tou;Yong Haur Tay;Phooi Yee Lau
Affiliations:
Computer Vision and Inteligent Systems (CVIS) group, Universiti Tunku Abdul Rahman (UTAR), Petaling Jaya, Selangor, Malaysia 46200;Computer Vision and Inteligent Systems (CVIS) group, Universiti Tunku Abdul Rahman (UTAR), Petaling Jaya, Selangor, Malaysia 46200;Computer Vision and Inteligent Systems (CVIS) group, Universiti Tunku Abdul Rahman (UTAR), Petaling Jaya, Selangor, Malaysia 46200 and Instituto de Telecomunicações, Lisboa, Portugal 104 ...
Venue:
Advances in Neuro-Information Processing
Year:
2009

Citing 0
Cited 1

Enhanced local binary covariance matrices (ELBCM) for texture analysis and object tracking

Proceedings of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

The two groups of popularly used texture analysis techniques for classification problems are the statistical and signal processing methods. In this paper, we propose to use a signal processing method, the Gabor filters to produce the feature images, and a statistical method, the covariance matrix to produce a set of features which show the statistical information of frequency domain. The experiments are conducted on 32 textures from the Brodatz texture dataset. The result that is obtained for the use of 24 Gabor filters to generate a 24 脳 24 covariance matrix is 91.86%. The experiment results show that the use of Gabor filters as the feature image is better than the use of edge information and co-occurrence matrices.