A Comparison of Active Classification Methods for Content-Based Image Retrieval
Philippe H. Gosselin, ETIS CNRS UMR8051, University of Cergy-Pontoise, ENSEA
Matthieu Cord, ETIS CNRS UMR8051, University of Cergy-Pontoise, ENSEA
This paper deals with content-based image indexing and category retrieval in general databases. Statistical learning approaches have been recently introduced in CBIR. Labelled images are considered as training data in learning strategy based on classification process. We introduce an active learning strategy to select the most difficult images to classify with only few training data. Experimentations are carried out on the COREL database. We compare seven classification strategies to evaluate the active learning contribution in CBIR.