Texture segmentation using different orientations of GLCM features

Authors:
Andrik Rampun;Harry Strange;Reyer Zwiggelaar
Affiliations:
Aberystwyth University, Wales, UK;Aberystwyth University, Wales, UK;Aberystwyth University, Wales, UK
Venue:
Proceedings of the 6th International Conference on Computer Vision / Computer Graphics Collaboration Techniques and Applications
Year:
2013

Citing 7
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Abstract

This paper describes the development of a new texture based segmentation algorithm which uses a set of features extracted from Grey-Level Co-occurrence Matrices. The proposed method segments different textures based on noise reduced features which are effective texture descriptor. Each of the features is processed including normalisation and noise removal. Principal Component Analysis is used to reduce the dimensionality of the resulting feature space. Gaussian Mixture Modelling is used for the subsequent segmentation and false positive regions are removed using morphology. The evaluation includes a wide range of textures (more than 80 Brodatz textures) and in comparison (both qualitative and quantitative) with state of the art techniques very good segmentation results have been obtained.