Illumination invariant unsupervised segmenter

Authors:
Michal Haindl;Stanislav Mikeš;Pavel Vácha
Affiliations:
Institute of Information Theory and Automation of the ASCR, Academy of Sciences CR, Prague, Czech Republic;Institute of Information Theory and Automation of the ASCR, Academy of Sciences CR, Prague, Czech Republic;Institute of Information Theory and Automation of the ASCR, Academy of Sciences CR, Prague, Czech Republic
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 7
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Abstract

A novel illumination invariant unsupervised multispectral texture segmentation method with unknown number of classes is presented. Multispectral texture mosaics are locally represented by illumination invariants derived from four directional causal multispectral Markovian models recursively evaluated for each pixel. Resulted parametric space is segmented using a Gaussian mixture model based unsupervised segmenter. The segmentation algorithm 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 large illumination invariant benchmark from the Prague Segmentation Benchmark using 21 segmentation criteria and compares favourably with an alternative segmentation method.