Bayesian fusion of color and texture segmentations

In many applications one would like to use information from both color and texture features in order to segment an image. We propose a novel technique to combine “soft” segmentations computed for two or more features independently. Our algorithm merges models according to a maximum descriptiveness criterion, and allows us to choose any number of classes for the final grouping. This technique also allows us to improve the quality of supervised classification based on one feature (e.g. color) by merging information from unsupervised segmentation based on another feature (e.g., texture)