Region-based object recognition: Pruning multiple representations and hypotheses

We address the problem of object recognition in computer vision. We rep-resent each model and the scene in the form of Attributed Relational Graph. A multiple region representation is provided at each node of the scene ARG to increase the representation reliability. The process of matching the scene ARG against the stored models is facilitated by a novel method for identi-fying the most probable representation from among the multiple candidates. The scene and model graph matching is accomplished using probabilistic relaxation which has been modified to minimise the label clutter. The exper-imental results obtained on real data demonstrate promising performance of the proposed recognition system.