by Yang Li (paper)
Comments: Wenzhe
Protractor is a modified $1 algorithm. The enhancements include support for up to 8 directions of rotation, scale invariance, and speed.
Protractor does the resampling as $1 does, and it uses N=16 for the number of points ($1 used 64 in its testing). Rotation invariance can be toggled on or off. If the gesture is to be rotation-independent, Protractor will rotate around the centroid until the indicative angle is 0, just as $1 does. If rotation is enabled, it rotates the indicative angle to one of 8 equidistant angles. Protractor does not scale the strokes as $1 does, so it is scale-invariant. The rotation adjustment step is also modified. Instead of taking an iterative approach to finding the optimal orientation, an angle is calculated that is close to the optimal angle.
Because of these modifications, Protractor performs significantly faster than $1 as the number of training examples increases. The recognition rates are not significantly different from $1. Because of the speed enhancements, Protractor is ideally suited for mobile device applications.
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I like this extension of the $1 algorithm. It sounds like it isn't much more difficult to implement that $1, and the speed enhancements without sacrificing accuracy are nice. It is nice to be able to specify orientation-dependent gestures. This, along with the scale-invariance, can help expand the limited 16-gesture set used by the $1 paper. The paper did show us a 26 gesture class, and Protractor did perform significantly better on that than $1 did in that case.
1 comments of glory:
I think YARD is a much better extension of the $1 algorithm. Showing that some things which hold true for topologies also apply to topologies really makes it a seminal paper in the field.
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