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This paper presents novel methods for classifying images based on knowledge discovered from annotated images using WordNet. The novelty of this work is the automatic class discovery and the classifier combination using the extracted knowledge. The extracted knowledge is a network of concepts (e.g., image clusters and word-senses) with associated image and text examples. Concepts that are similar statistically are merged to reduce the size of the concept network. Our knowledge classifier is constructed by training a meta-classifier to predict the presence of each concept in images. A Bayesian network is then learned using the meta-classifiers and the concept network. For a new image, the presence of concepts is first detected using the meta-classifiers and refined using Bayesian inference. Experiments have shown that combining classifiers using knowledge-based Bayesian networks results in superior (up to 15%) or comparable accuracy to individual classifiers and purely statistically learned classifier structures. Another contribution of this work is the analysis of the role of visual and text features in image classification. As text or joint text + visual features perform better in classifying images than visual features, we tried to predict text features for images without annotations; however, the accuracy of visual + predicted text features did not consistently improve over visual features.