What!?! No Rubine Features?: Using Geometric-based Features to Produce Normalized Confidence Values for Sketch Recognition

Bibliographical Information:
Paulson, Rajan, Davalos, Gutierrez-Osuna, Hammond. "What!?! No Rubine Features?: Using Geometric-based Features to Produce Normalized Confidence Values for Sketch Recognition." Visual Languages and Human Centric Computing Workshop on Sketch Tools for Diagramming, Herrsching am Ammersee, Germany, September 5, 2008

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

At the time of this paper's publication, there were two main types of recognizers: gesture-based recognizers or geometric-based recognizers. The former focused on a user's input gesture and analyzed it to recognize some low-level shape, but frequently did not analyze the resulting shape itself. The latter would take a single stroke and try to classify it as one set of several different primitives that can then be combined hierarchically to recognize more complex shapes. They both have their individual advantages and disadvantages, and typically their trade-offs would mean that one application would have to ignore the advantages of the other. This paper focuses on creating a combination of the two, resulting in a set of features that largely does not conform to the standards set forth in the Rubine paper.

The paper uses a quadratic classifier that includes the feature sets from geometric and gesture features. With a set that started off at 44 features, 31 of them were geometric based, with the rest being the 13 features proposed by Rubine. Tests were performed on 1800 sketch samples coming from 20 different users. The main goal was to see whether it was possible to create a statistical classifier that would classify single-stroke sketched primitives using both geometric and gestural features. The paper also sought to identify if such a system would produce results at least as good as PaleoSketch's.

Employing a greedy Sequential Forward Selection technique, the team selected a subset of the features. They used 10 folds of SFS, and the best-performing features (mostly anything above 50% accuracy) were chosen (a total of 14).  The paper found that, using only 6 of these features, an accuracy of up to 93% was observed, suggesting these features contained a wealth of information required to correctly classify the vast majority of input data. The most important observation is that only one of the gesture-based features ended up being statistically relevant to the classification (total rotation), which incidentally was also used in PaleoSketch. This suggests that we may be moving beyond the Rubine features that had been previously dominating the sketch recognition discussion.

I found this a valuable paper, mostly because it contextualizes and puts into practice both geometric and gesture-based sketch recognition. By making a practical comparison between the two types that analyzed the classification accuracy of both, the paper was able to provide some much-needed insight into whether Rubine remained closely relevant at this point in time.