Meta-classifiers for multimodal document classification
This paper proposes learning algorithms for the problem of multimodal document classification. Specifically, we develop classifiers that automatically assign documents to categories by exploiting features from both text as well as image content. In particular, we use meta-classifiers that combine state-of-the-art text and image based classifiers into making joint decisions. The two meta classifiers we choose are based on support vector machines and Adaboost. Experiments on real-world databases from Wikipedia demonstrate the benefits of a joint exploitation of these modalities.