Content-based image retrieval can be dramatically improved by providing a good initial database overview to the user. To address this issue, we present in this paper the Adaptive Robust Competition. This algorithm relies on a non-supervised database categorization, coupled with a selection of prototypes in each resulting category. In our approach, each image is represented by a high-dimensional signature in the feature space, and a principal component analysis is performed for every feature to reduce dimensionality. Image database overview is computed in challenging conditions since clusters are overlapping with outliers and the number of clusters is unknown.
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