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.
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Haindl, M., Mikeš, S. (2005). Colour Texture Segmentation Using Modelling Approach. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_54
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DOI: https://doi.org/10.1007/11552499_54
Publisher Name: Springer, Berlin, Heidelberg
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