Reading #6: Protractor: A Fast and Accurate Gesture Recognizer (Li)

Comment Location:
http://martysimpossibletorememberurl.blogspot.com/2010/09/reading-6-protractor.html

Summary:
This the third recent paper we have done so far; this one was done by a person working research at Google.  Protractor, the template-recognizing program in question, claims to be small in size and fast in speed.  The author gives arguments of the superiority of templates over parameter-based programs; the author argued the templates could be customized for the user and the results would be excellent, whereas such a procedure did not occur with parameter-based programs.  Protractor uses a nearest neighbor approach. 
Protractor (assuming the user allows it) aligns the image and reduces noise to allow for faster matching (it does not rescale, unlike the $1 recognizer).  I had difficulty understanding the meat of the classification procedure and would appreciate it if someone could explain it to me in a simple manner.  Protractor did not demonstrate significantly greater results than $1, but Protractor performed much more quickly.


Discussion:
The author's arguments favoring templates failed to mention their flaws.  Protractor certainly brings some unique ideas to the table, but it is worrisome how the author stated on page 1 that Protractor would work if the templates were customized for the user.  This indicates the program is not very robust.  The data set is worrisome as well.  The author employed a large data set that concentrated on the $1's strengths (where it outperformed Rubine); Protractor did not demonstrate it could compensate for $1's shortcomings.

To summarized, Protractor did what the author wanted: it's a faster version of $1.  However, Protractor is not a significant improvement over $1 in terms of the success rate--just a slight improvement.