Trainable context model for multiscale segmentation

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Abstract
Most previous approaches to Bayesian segmentation have used simple prior models, such as Markov random fields (MRF), to enforce regularity in the segmentation. While these methods improve classification accuracy, they are not well suited to modeling complex contextual structure. In this paper, we propose a context model for multiscale segmentation which can capture very complex behaviors on both local and global scales. Our method works by using binary classification trees to model the transition probabilities between segmentations at adjacent scales. The classification trees can be efficiently trained to model essential aspects of contextual behavior. In addition, the data model in our approach is novel in the sense that it can incorporate the correlation among the wavelet feature vectors across scales. We apply our method to the problem of document segmentation to illustrate its usefulness.

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