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To perform a semantic search on a large dataset of images, we need to be able to transform the visual content of images (colors, textures, shapes) into semantic information. This transformation, called image annotation, assigns a caption or keywords to the visual content in a digital image. In this paper we try to
. The color histograms, Texture, GIST and invariant moments, used as features extraction methods, are combined together with multiclass support vector machine, Bayesian networks, Neural networks and nearest neighbour classifiers, in order to annotate the image content with the appropriate keywords. The accuracy of the
database is annotated with keywords. We present and evaluate a new method which improves the effectiveness of content-based image retrieval, by integrating semantic concepts extracted from text. Our model is inspired from the probabilistic graphical model theory: we propose a hierarchical mixture model which enables to handle
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