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

Using Geometric-based Features to Produce Normalized Confidence Values for Sketch Recognition

Summary:

This paper proposes a hybrid recognition scheme by combining Gesture-based recognition and Geometric-based recognition together. With the hybrid recognition scheme, highly accurate classification will be achieved while maintaining user independence and allowing users to draw freely.

Sketch Recognition Methods:

In general, there are two approaches for sketch recognition. One is gesture-based. The other is geometric-based. Gesture-based recognition focuses on how a sketch is drawn. It takes the sampling points(x,y,t) of a stroke as input and then classifies the stroke into a set of pre-defined gestures. This kind of recognition scheme is fast but it needs user-dependent feature sets and requires individual training by each user. Geometric-based recognition focuses on what a sketch looks like. So it is more user-independent. However, geometric-based recognizer usually uses numerous thresholds and heuristic hierarchies which are hard to analyze and optimize in a systematic fashion.

Unlike gestural recognizers using statistical classifiers, geometric recognizer uses error matrix to compare a sketched shape and its ideal version with a series of geometric tests and formulas.

Hybrid Recognition Scheme:

The hybrid recognition scheme remains the strength and avoids the drawbacks of the two recognition methods mentioned in last section by taking a few features from each of the two methods. The overall picture of all features in hybrid recognizer is as follows,

The first 31 features are geometric features. The last 13 features are Rubine gestural features. The bold ones are the optimal feature set after feature subset selection using sequential forward selection technique. It is discovered that gestural features are less significant in aiding freely sketch recognition.

Bibliography:

Paulson, Brandon, Pedros Devalos, Pankaj Rajan, Ricardo Guitierrez, and Tracy Hammond. "Texas A&M : OBJECT 1237321989 : Hammond Cv." Texas A&M : OBJECT 1237321989 : Hammond Cv. N.p., n.d. Web. 12 Feb. 2013.