FOSC-X uses bounded dynamic programming to compute top-M optimal non-horizontal cuts from clustering hierarchies in linear time, with or without cluster-count constraints.
Relative clustering validity criteria: A comparative overview
2 Pith papers cite this work. Polarity classification is still indexing.
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Empirical tests on 148 images show that the best colorspace for k-means quantization depends on the image and the target number of colors k, with RGB winning in roughly half the cases.
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FOSC-X: An Extended Framework for Optimal Local Cuts and Non-Horizontal Cluster Selection from Clustering Hierarchies
FOSC-X uses bounded dynamic programming to compute top-M optimal non-horizontal cuts from clustering hierarchies in linear time, with or without cluster-count constraints.
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Optimized $k$-means color quantization of digital images in machine-based and human perception-based colorspaces
Empirical tests on 148 images show that the best colorspace for k-means quantization depends on the image and the target number of colors k, with RGB winning in roughly half the cases.