Image database clustering with SVM-based class personalization

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Image database clustering with SVM-based class personalization
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 f...
Bertrand Le Saux, Nozha Boujemaa
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Authors Bertrand Le Saux, Nozha Boujemaa
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