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Abstract: We derive real-time global optimization methods for several clustering optimization problems commonly used in unsupervised texture segmentation. Speed is achieved by exploiting the image neighborhood relation of features to design a multiscale optimization technique, while accuracy and global optimization properties are gained using annealing techniques. Coarse grained cost functions are derived for central and histogram-based clustering as well as several sparse proximity-based clustering... (Update)
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BibTeX entry: (Update)
J. Puzicha and J. Buhmann, "Multiscale annealing for grouping and unsupervised texture segmentation," Computer Vision and Image Understanding, vol. 76, pp. 213--230, 1999. http://citeseer.ist.psu.edu/puzicha99multiscale.html More
@article{ puzicha99multiscale,
author = "Jan Puzicha and Joachim M. Buhmann",
title = "Multiscale Annealing for Grouping and Unsupervised Texture Segmentation",
journal = "Computer Vision and Image Understanding: {CVIU}",
volume = "76",
number = "3",
pages = "213--230",
year = "1999",
url = "citeseer.ist.psu.edu/puzicha99multiscale.html" }
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