A generic framework for colour texture segmentation

A generic framework for colour texture segmentation Purpose – The purpose of this paper is to propose a generic framework based on the colour and the texture features for colour‐textured image segmentation. The framework can be applied to any real‐world applications for appropriate interpretation. Design/methodology/approach – The framework derives the contributions of colour and texture in image segmentation. Local binary pattern and an unsupervised k ‐means clustering are used to cluster pixels in the chrominance plane. An unsupervised segmentation method is adopted. A quantitative estimation of colour and texture performance in segmentation is presented. The proposed method is tested using different mosaic and natural images and other image database used in computer vision. The framework is applied to three different applications namely, Irish script on screen images, skin cancer images and sediment profile imagery to demonstrate the robustness of the framework. Findings – The inclusion of colour and texture as distributions of regions provided a good discrimination of the colour and the texture. The results indicate that the incorporation of colour information enhanced the texture analysis techniques and the methodology proved effective and efficient. Originality/value – The novelty lies in the development of a generic framework using both colour and texture features for image segmentation and the different applications from various fields. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Sensor Review Emerald Publishing

A generic framework for colour texture segmentation

Sensor Review, Volume 30 (1): 11 – Jan 26, 2010

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Publisher
Emerald Publishing
Copyright
Copyright © 2010 Emerald Group Publishing Limited. All rights reserved.
ISSN
0260-2288
DOI
10.1108/02602281011010817
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to propose a generic framework based on the colour and the texture features for colour‐textured image segmentation. The framework can be applied to any real‐world applications for appropriate interpretation. Design/methodology/approach – The framework derives the contributions of colour and texture in image segmentation. Local binary pattern and an unsupervised k ‐means clustering are used to cluster pixels in the chrominance plane. An unsupervised segmentation method is adopted. A quantitative estimation of colour and texture performance in segmentation is presented. The proposed method is tested using different mosaic and natural images and other image database used in computer vision. The framework is applied to three different applications namely, Irish script on screen images, skin cancer images and sediment profile imagery to demonstrate the robustness of the framework. Findings – The inclusion of colour and texture as distributions of regions provided a good discrimination of the colour and the texture. The results indicate that the incorporation of colour information enhanced the texture analysis techniques and the methodology proved effective and efficient. Originality/value – The novelty lies in the development of a generic framework using both colour and texture features for image segmentation and the different applications from various fields.

Journal

Sensor ReviewEmerald Publishing

Published: Jan 26, 2010

Keywords: Image processing; Adaptive system theory; Colours technology; Cluster analysis; Smoothing methods

References

  • Algorithms for clustering data
    Jain, A.K.; Dubes, R.C.
  • Texture classification using texture spectrum
    Wang, L.; He, D.

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