Probabilistic Shape Parsing for View-Based Object Recognition

We present a novel probabilistic model for parsing shapes into several distinguishable parts for accurate shape recognition. This shape parsing is based on robust geometric features that permit high recognition accuracy. Although modelling shapes is an inherently uncertain process, our approach is lenient, in that the desired parse of a shape only needs to be within its k most probable parses. Using this set of shape decompositions, we can improve recognition accuracy even further by determining which parts of a shape are common across most views of objects in the same class.