What!?! No Rubin Features?: Using Geometric-based Features to Produce Normalized Confidence Values for Sketch Recognition (Paper Report)

Bibliography:

Paulson, Brandon, et al. "What!?! no Rubine features?: using geometric-based features to produce normalized confidence values for sketch recognition." HCC Workshop: Sketch Tools for Diagramming. 2008.

Link: 

http://srl.tamu.edu/srlng/research/paper/23?from=/srlng/research/

Summary:

In this paper the authors create and test a system that uses both gesture and geometric-based features for sketch recognition of complex shapes. Gesture recognition depends on how the user draws whereas geometric-based recognition depends on what the user draws. Their aim is to create a recognition system that was user-independent. This system was tested against the Paleosketch recognizer and the differences between the two were shown to be statistically insignificant. The classifier they used was a statistical classifier. The total angle feature from gesture recognition was the only significant feature chosen for the optimal subset of features.

Comments:

It is interesting that these two methods of classification can be combined and used so effectively, drawing on the strengths of both methods. I didn't understand the significance of the way the user groups were split and their effect on the validation. Also the way the subset of optimal features was found.

Research Ideas:

I wonder why there were so few gestural features included in the subset of optimal features.