Histogram Clustering for Unsupervised Image Segmentation

This paper introduces a novel statistical mixture model for probabilistic grouping of distributional 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 e cient multiscale formulation is developed. We present a prototypical application of this method for the unsupervised segmentation of textured images based on local distributions of Gabor coe cients. Benchmark results indicate superior performance compared to K means clustering and proximity-based algorithms.

• Type: Conference
• Year: 1999
• Where: CVPR
• Authors: Jan Puzicha, Joachim M. Buhmann, Thomas Hofmann
• Keywords: Cient Multiscale Formulation, Computer Vision, CVPR 1999, Distributional Histogram Data, Gabor Coe Cients, Optimal Clustering Solutions, Posteriori Estimation