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

Brand Paulson, Pankaj Rajan, Pedro Davalos, Ricardo Gutierez-Osuna, and Tracy Hammond

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Summary

The authors sought to investigate what gesture- and geometric-based features are best for recognizing sketches. The geometric features examined came from PaleoSketch; while, the gesture features came from Rubine. A quadratic classifier was used to classify strokes based on the features.

The evaluation used 1800 examples, where 900 where for training and subset selection, and the other 900 were for testing. A greedy, sequential forward selection technique was used to determine feature subsets. The optimal features subset contained 15 features, only one of which came from Rubine.The optimal feature subset reported accuracies similar, but slightly smaller than the original PaleoSketch.

Discussion

This work is beneficial to research investigating hybrid approaches for recognizing sketches using both geometric and gesture based features. One possible issue with this work, is that the training and test examples looked more suited to geometric based approaches than gesture based. Adding more gestural based examles such asalphabet characters to the set of classes may see improved performance in Rubine feature, or may not. An investigation of this would be one direction of future work.