Conference paper

Haindl M., Mikeš S., Pudil P.

Multiple Classifier Systems, LNCS 5519

Springer

Berlin Heidelberg

2009

3-642-02325-8

0302-9743

RNDr. Petr Somol, Ph.D. (ÚTIA - Oddělení rozpoznávání obrazů)

unsupervised image segmentation

An unsupervised multi-spectral, multi-resolution, multiple-segmenter for textured images with unknown number of classes is presented. The segmenter is based on a weighted combination of several unsupervised segmentation results, each in different resolution, using the modified sum rule. Multi-spectral textured image mosaics are locally represented by four causal directional 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 leading alternative image segmentation methods.