Improving performance of interactive categorization of images using relevance feedback

When using relevance feedback for the interactive categorization of images, the strategy employed by the system to select images to be presented to the user is of paramount importance for overall performance. Using SVM-based relevance feedback, we present a new selection criterion, based on the active learning principle, that minimizes redundancy between the candidate images shown to the user at every round. We also emphasize the fact that insensitivity to the scale of the target classes in the description space is an important quality of the learner in the interactive categorization context and we propose specific kernel functions to achieve this. Experimental results on several image databases confirm the attractiveness of our suggestions.