Unsupervised texture segmentation using multiple segmenters strategy

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:
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Year:
2007

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Abstract

A novel unsupervised multi-spectral multiple-segmenter texture segmentation method with unknown number of classes is presented. The unsupervised segmenter is based on a combination of several unsupervised segmentation results, each in different resolution, using the sum rule. Multi-spectral texture mosaics are locally represented by four causal multi-spectral random field models recursively evaluated for each pixel. The single-resolution segmentation part of the 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 the commonest segmentation criteria and compares favourably with several alternative texture segmentation methods.