Histogram clustering for unsupervised segmentation and image retrieval

Jan Puzicha, Thomas Hofmann, Joachim M. Buhmann
1999 Pattern Recognition Letters  
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 of
more » ... r coefficients. Benchmark results indicate superior performance compared to ¦ -means clustering and proximity-based algorithms. In a second application the histogram clustering method is utilized to structure image databases for improved image retrieval.
doi:10.1016/s0167-8655(99)00056-2 fatcat:gkcwzowcyfbulcpplixy6oyyna