Optimal Mean-Precision Classifier
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Hierarchical multiple Markov chain model for unsupervised texture segmentation
IEEE Transactions on Image Processing
Unsupervised texture segmentation using multiple segmenters strategy
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Illumination invariant unsupervised segmenter
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Computer Vision and Image Understanding
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A new unsupervised multispectral texture segmentation method with unknown number of classes is presented. Multispectral texture mosaics are locally represented by four causal multispectral random field models recursively evaluated for each pixel. 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 the commonest segmentation criteria and compares favourably with several alternative texture segmentation methods.