Combining corners from multiple segmenters.

Paper
Wolin, Aaron, Martin Field, and Tracy Hammond. "Combining corners from multiple segmenters." Proceedings of the Eighth Eurographics Symposium on Sketch-Based Interfaces and Modeling. ACM, 2011.
Publication Link: http://dl.acm.org/citation.cfm?id=2021185

Summary

This work proposes a very interesting meta-classifier for corner finding. By treating the outputs of an ensemble of corner-finding classifiers as features (candidates for corners in a given sketch), the authors develop a corner subset selection process using a weighted error metric to select the best subset of features (which ideally should be the corners contained in the sketch).
They test and compare the performance of their classifier with existing methods to show an improvement in all-or-nothing accuracy.


Discussion

Pros

I like the approach of treating corner candidates as features and selecting the best set.

Cons.

I wonder how this system will work with n-1 corner finding classifiers. I either missed that or it isn't included in the work. The authors motivate the advantage of each classifier, but can they perform just as well or better (or worse) with one less classifier?