Histogram clustering for unsupervised segmentation and image retrieval
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 Gabor coefficients. Benchmark results indicate superior performance compared to K-means clustering and proximity-based algorithms. In a second application the histogram clustering method is utilized to structure image databases for improved image retrieval. (C) 1999 Elsevier Science B.V. All rights reserved.
histogram clustering image retrieval texture segmentation multiscale annealing