liwenzhe
Summary:
97% accuracy with only one provided example. Such is the boast of the $1 Recognizer. This paper by Jacob Wobbrock, Andrew Wilson, and Yang Li (the man behind Protractor) explains the implementation and reasoning behind the creation of an easy and cheap recognizer designed for novice designers. The authors stress that gesture-based interaction can be very useful, but that it was previously difficult for non-experts in the field to implement a system. The authors even provide this very nice summary of contributions:
1. be resilient to variations in sampling due toSo what are the limitations of this $1 Recognizer? Because it is rotation, scale, and position invariant, it cannot tell the difference between squares and rectangles, ovals and circles, vertical and horizontal lines, etc. In some instances, the differences between these sorts of things may be critical, and the authors are quick to stress this to readers. By testing $1 against Rubine and Dynamic Time Warping (not a form of space travel), the authors found that their solution was highly accurate. Not too shabby for a $1 charge.
movement speed or sensing;
2. support optional and configurable rotation, scale, and
position invariance;
3. require no advanced mathematical techniques (e.g.,
matrix inversions, derivatives, integrals);
4. be easily written in few lines of code;
5. be fast enough for interactive purposes (no lag);
6. allow developers and application end-users to
“teach” it new gestures with only one example;
7. return an N-best list with sensible [0..1] scores that
are independent of the number of input points;
8. provide recognition rates that are competitive with
more complex algorithms previously used in HCI.
Discussion:
The authors themselves brought up my own biggest issue - was the $1 Recognizer actually easy for novice users to implement? As of this paper, that fact was yet to be determined. If the whole focus was to help more people integrate and use gesture recognition, then it might be important to actually have some people implement it and evaluate it themselves. Regardless, I think that by using a few simple modification tricks, the $1 Recognizer is a great example of gesture recognition for the laymen programmer.
Yes, I agree, to lose the information of transformation is not a good idea, you will not be able to tell circle or ovals, etc. Even though the alignment step is necessary, it sacrificed too much information which could be crucial for a complex stroke.
ReplyDeleteWow, good point. I guess you can't conclude that an algorithm is easy to use if you really don't know who's using it. Though, as a person with very little background in gesture recognition, I do think that the $1 recognizer sounds pretty simple on paper. Perhaps the authors should have tested how quickly novices could implement $1 as well :)
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