Multiscale Annealing for Grouping and Unsupervised Texture Segmentation
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摘要
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 methods. For optimization deterministic annealing algorithms are applied. Annealing schedule, coarse-to-fine optimization and the estimated number of segments are tightly coupled by a statistical convergence criterion derived from computational learning theory. The notion of optimization scale parametrized by a computational temperature is thus unified with the scales defined by the image resolution and the model or segment complexity. The algorithms are benchmarked on Brodatz-like microtexture mixtures. Results are presented for an autonomous robotics application. Extensions are discussed in the context of prestructuring large image databases valuable for fast and reliable image retrieval.
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论文评审过程:Received 21 October 1998, Accepted 21 September 1999, Available online 2 April 2002.
论文官网地址:https://doi.org/10.1006/cviu.1999.0805