Document image analysis for digital libraries
Proceedings of the 2006 international workshop on Research issues in digital libraries
Hierarchical Ensemble Support Cluster Machine
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
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We classify an image by generating a list of salient visual features present in the luminance channel, and matching the resulting variable-length feature list to categoryspecific generative models for such features. To facilitate quick computation, we use thresholded Viola-Jones rectangular features, each represented by a five-dimensional descriptor. For each image category, a probability distribution for feature-lists is given by a latent conditional independence (LCI) model and classification is maximum likelihood. On the NIST tax forms database [3], where intracategory variations include variable scan-lightness, skew, noise, and machine-printed form-filling, our method improves performance over published results, while requiring very little training data, and without relying on an extensive set of handcrafted features.