An effective active learning method for interactive content-based retrieval in remote sensing images

This paper presents a novel active learning (AL) technique to drive relevance feedback in content based image retrieval (CBIR) from earth observation data archives. The proposed AL method aims at defining an effective set of relevant and irrelevant images with respect to the query image as small as possible. This is achieved on the basis of a joint evaluation of three criteria: i) uncertainty, ii) diversity and iii) density of images. The uncertainty and diversity criteria aims at choosing the most informative images in the archive, whereas the density criterion aims at selecting those that are representative of the underlying distribution of images in the archive. In the proposed AL method, the three criteria are applied in two consecutive steps. In the first step the most uncertain images are selected based on well-known margin sampling strategy. In the second step the images that are associated to high density regions in the archive and are diverse (i.e., distant) to each other are chosen from the most uncertain ones on the basis of a novel clustering based strategy. Experimental results show the effectiveness of the proposed AL method, particularly when a poor initial set of relevant and irrelevant images is available.