A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2004; you can also visit the original URL.
The file type is
This paper introduces a novel statistical latent class model for probabilistic grouping of distributional and histogram data. Adopting the Bayesian framework, we propose to perform annealed maximum a posteriori estimation to compute optimal clustering solutions. In order to accelerate the optimization process, an efficient multiscale formulation is developed. We present a prototypical application of this method for unsupervised segmentation of textured images based on local distributions ofdoi:10.1016/s0167-8655(99)00056-2 fatcat:gkcwzowcyfbulcpplixy6oyyna