Summary:
Paulson's presentation of MARQS included an overview of the program and the possible advantages and disadvantages of its use. MARQS, unlike all previous gesture recognizers, uses only 5 features and a single training example. This is accomplished through seperate single and multiple classifiers that read a specific variation from the gesture made and the gesture base and choose the best match. As more data is accrued for these symbols it will gradually get more accurate, but only 1 starting example is necessary. MARQS also is stroke-number independent, as well as domain independent, allowing users to draw something in many different ways and still have it recognized correctly by the system. Orientation and localization are also independent, allowing for even further variation by the user.
Discussion:
As Brian discussed, while decently effective for such a small amount of training data, some program features are traded for user tolerance. While the system may recognize a stick man with his arms up and a stick man with his arms down both as a stick man, it also recognizes a happy face and a sad face as both faces. As discussed, this could be overcome with multiple layers of classification, though. I think this is a GREAT idea. I love the tolerance in this program, and adding the ability to draw gestures and then classify them is a great designer tool. Sadly, I think the notebook idea is somewhat overkill, because most "notebooks" are not consistent files. The example of drawing some waves of water to go to the "water" notes is nice, but rarely will there be notes all about water in one place. Usually notes are broken up into dates, which are easier to recognize and find by keystrokes, and if they ARE separate the user has already sorted the notes. The only practical use of the system that I could see is if someone were to take an exhorbitant amount of notes and they were classifed into different subjects.
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The whole entire time I was focusing on MARQS applications, I had completely forgotten about the algorithm's use in the notebook application. You did bring up some decent points about potential downfalls with the notebook application. I guess I see the algorithm being useful when handled in another way. With the waves example, suppose the waves were used in certain places as an easy way to remember those places had a strong connection with waves to the user. In addition, if the user typed waves a lot, it would be cumbersome to do a text search of all cases of waves to find a particular section that the user was interested in. But the waves drawing would be concentrated in areas that the user would be most interested in. In such cases, the algorithm would be pretty useful.
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