Colour texture segmentation using modelling approach

Authors:
Michal Haindl;Stanislav Mikeš
Affiliations:
Institute of Information Theory and Automation, Academy of Sciences CR, Prague, Czech Republic;Institute of Information Theory and Automation, Academy of Sciences CR, Prague, Czech Republic
Venue:
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Year:
2005

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Abstract

A fast and robust type of unsupervised multispectral texture segmentation method with unknown number of classes is presented. Single decorrelated monospectral texture factors are represented by four local autoregressive random field models recursively evaluated for each pixel and for each spectral band. The segmentation algorithm is based on the underlying Gaussian mixture model and starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached. The performance of the presented method is extensively tested on the Prague segmentation benchmark using nineteen most frequented segmentation criteria.