Integration of active learning in a collaborative CRF

We present an active learning approach for visual multiple object class recognition, using a conditional random field (CRF) formulation. We name our graphical model dasiacollaborativepsila, because it infers class posteriors in in stances of occlusion and missing information by assessing the joint appearance and geometric assortment of neighboring sites. The model can handle scenes containing multiple classes and multiple objects inherently while using the confidence of its predictions to enforce label uniformity in areas where evidence supports similarity. Our method uses classification uncertainty to dynamically select new training samples to retrain the discriminative classifiers used in the CRF. We demonstrate the performance of our approach using cluttered scenes containing multiple objects and multiple class instances.