Bayesian view class determination

Anjali Pathak, Octavia I. Camps

Abstract: A Bayesian approach to the view class determination problem is presented. The view classes used contained probabilistic information that takes into account both geometrical and illumination characteristics. The test images match best or second best to the correct view class in approximately 80% of the cases and above 90% of the cases, respectively. The images that fail to be correctly classified correspond to views near the boundaries of the clusters. Even though these views have the same segments as the rest of the views in their class, they look significantly different. This suggests that different definitions of clustering should be studied.<