Semi-supervised learning and discovery of unkown structures among data: Application to satellite image annotation

In this paper, we present a semi-supervised method for auto-annotating image collections and discovering unknown structures among them. The approach relies on the existence of only a small training database of annotated examples. First, a fully-supervised algorithm using annotated samples is presented. Next, we introduce a semi-supervised procedure which allows us to incorporate unannotated samples and to infer the existence of unknown structures, that is, the existence of new image classes which are not represented in the training database. Finally, we present experimental results from a database of satellite images and briefly mention the possibility of reusing the presented approach as a basis for more complex systems such as Content Based Image Retrieval (CBIR) systems.