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Painter identification using local features and naive Bayes

The goal of this paper is to offer a framework for image classification "by type". For example, one may want to classify an image of a certain office as man-made - as opposed to outdoor scene, even if no image of a similar office exists in the training set. This is accomplished by using local features, and by using the naive Bayes classifier. The application presented here is classification of paintings; after the system is presented with a sample of paintings of various artists, it tries to determine who was the painter who painted it. The result is local - each small image block is assigned a painter, and a majority vote determines the painter. The results are roughly visually consistent with human perception of various artists' style.