The first extension uses clustering on the underlying local context features in order discover features that recur within object classes. The modification improves performance for a rigid, generic, and loosely structured classes, but it does not reliably discover recurrent features. Further analysis shows most of the performance improvement comes from a side-effect of the clustering algorithm. Namely, features that tend to create noise in the system become marginalized.
The second extension takes a principled approach to estimating the quality of each object model feature based on its robustness and commonness. Noisy features get a low quality score and thus contribute less noise the recognition process. This approach further improves recognition for rigid, generic, and loosely structured object classes over the clustering method.