Distances Based on Non-rigid Alignment for Comparison of Different Object Instances

Comparison of different object instances is hard due to the large intra-class variability. Part of this variability is due to viewpoint and pose, another due to subcategories and texture. The variability due to mild viewpoint changes, can be normalized out by aligning the samples. In contrast to the classical Procrustes distance, we propose distances based on non-rigid alignment and show that this increases performance in nearest neighbor tasks. We also investigate which matching costs and which optimization techniques are most appropriate in this context.