K-automatic discovery in large image databases

In this paper, we present an unsupervised grouping approach of data items (images) in the context of content-based exploration of large image databases. More particularly, we highlight a partition clustering method, which proposes an experimental solution to the famous problem of automatic discovery of the number of clusters (k). The majority of partition clustering methods consider the manual valuation of k. Manual valuation of k may be interesting for specific domains of applications where the expert has an accurate idea of the number of clusters he wants, however it is unrealistic for generic applications, and needs important estimation efforts without any insurance of their efficiencies. MOTS-CLES : Regroupement, partition, K, nombre de groupes, image, bases de données.