Texture classification via conditional histograms

Authors:
Eugenia Montiel;Alberto S. Aguado;Mark S. Nixon
Affiliations:
Electronic and Electrical Engineering, University of Surrey, Guildford, Surrey GU2 5XH, United Kingdom;Electronic and Electrical Engineering, University of Surrey, Guildford, Surrey GU2 5XH, United Kingdom;Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
Venue:
Pattern Recognition Letters
Year:
2005

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Abstract

This paper presents a non-parametric discrimination strategy based on texture features characterised by one-dimensional conditional histograms. Our characterisation extends previous co-occurrence matrix encoding schemes by considering a mixture of colour and contextual information obtained from binary images. We compute joint distributions that define regions that represent pixels with similar intensity or colour properties. The main motivation is to obtain a compact characterisation suitable for applications requiring on-line training. Experimental results show that our approach can provide accurate discrimination. We use the classification to implement a segmentation application based on a hierarchical subdivision. The segmentation handles mixture problems at the boundary of regions by considering windows of different sizes. Examples show that the segmentation can accurately delineate image regions.