DocuBrowse: faceted searching, browsing, and recommendations in an enterprise context
Proceedings of the 15th international conference on Intelligent user interfaces
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We address document image classification by visual ap- pearance. An image is represented by a variable-length list of visually salient features. A hierarchical Bayesian net- work is used to model the joint density of these features. This model promotes generalization from a few samples by sharing component probability distributions among differ- ent categories, and by factoring out a common displace- ment vector shared by all features within an image. The Bayesian network is implemented as a factor graph, and parameter estimation and inference are both done by loopy belief propagation. We explain and illustrate our model on a simple shape classification task. We obtain close to 90% accuracy on classifying journal articles from memos in the UWASH-II dataset, as well as on other classification tasks on a home-grown data set of technical articles.