To allow efficient browsing of large image collections, we have to provide a summary of its visual content. We present in this paper a robust approach to organize image databases: the Adaptive Robust Competition (ARC). This algorithm relies on a non-supervised database categorization, coupled with a selection of prototypes in each resulting category. This categorization is performed using image descriptors, which describe the visual appearance of the images. A principal component analysis is performed for every feature to reduce dimensionality. Then, clustering is performed in challenging conditions by minimizing a Competitive Agglomeration objective function with an extra noise cluster to collect outliers. The competition is improved to be adaptive to clusters of various densities. In a second step, we provide the user with tools to correct possible misclassifications and personalize the image categories. The constraints to deal with for such a system are the simplicity of the user feedback and the rapidity to propose a new category based on the user's criteria.