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Computer Science @ Rochester
Friday, July 25, 2003
11:00 AM
CSB 209
Isaac Green
University of Rochester
Experiments in Generic Object Recognition
The task of generic object recognition involves learning to identify members of a class of objects based on a few exemplars from that class. Generic objects classes are inherently ill-defined. Objects can be grouped into classes based on varying criteria such as form, function, color, size, etc. In this work, we develop two extensions to a well-studied, 3D view-based, rigid-object recognizer that improve its performance on generic object classes grouped on shape and a related class of objects we call loosely structured objects.

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.