What!?! no Rubine features?: using geometric-based features to produce normalized confidence values for sketch recognition.

Paper
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.
Direct Link: https://www.cs.auckland.ac.nz/research/conferences/skekchws/proceedings/vlhcc_stws_p57.pdf

Summary

This work develops a novel method to provide uniform confidence measurements to geometrically recognized complex shapes. To achieve this, the authors use a combination of geometric and gesture based features with a quadratic classifier to recognize single stroke primitives such as lines, ellipses, helix etc. They apply feature subset selection to reduce a 44 dimension feature set to about 9 dimensions and provide a direction for future application of their work.

Discussion

Pros

It is an interesting approach to providing confidence measures to geometric features.
The use of feature selection methods resulted in fairly high accuracy, but more important is the demonstration of useful confidence measures in addition to high accuracy.
The paper was very well written.

Cons.

I find it odd that the authors dedicated a full page or more to describing sequential forward selection technique in feature subset selection. To me, it diverted attention away from the primary objective of the work, which was in developing accurate geometric based features with reliable, uniform confidence measures.

They also failed to elaborate on performance. I mean, the primary purpose was not really recognizing but giving confidence estimates. This ability is not at all discussed or shown in their results section. They spent way too much time talking about SFS...