Summary:
Kadous created TClass, which uses metafeatures of gesture data to form a syntactic representation of a gesture.
The system was tested on two data sets: Auslan and Nintendo data. Auslan is an Australian sign language that has signs not like ASL, although the overall ideas of hand shape, location, and movement are present in the language. The Nintendo data comes from a Powerglove test set comprised of 95 basic hand movements.
Tests were conducted using a Powerglove (P5) and a Flock of Birds. On Kadous's tests, the initial error rate for TClass was extremely high compared to the best error rates (for both sets of data). Using AdaBoost, the system's accuracy became more tolerable, but it was never as good as a fine, hand-picked set of features.
Discussion:
I have mixed feelings about this system. I like the addition of metafeatures that are readable with TClass, but I also don't quite know what to make of the system's poor accuracy in some cases. The accuracy results presentation was confusing, since the author gave horrible results first, then semi-poor results after when using AdaBoost, but the horrible results also included a TClass with AdaBoost (AB) field, so what the hell is going on? Also, the explanation that the Nintendo dataset is "hard" does not fly; if a "naive" algorithm beats you , you cannot say that poor results are because of the test set.
Nevertheless, I think that research in this area of trying to find both accurate and understandable results is worthwhile.
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1 comment:
Yes, the metafeatures aspect was a refreshing concept in the paper. It's quite novel, and would be interesting to expand on. The less than stellar results were a bit unfortunate, but I at least expected some sound reasons behind those poor results. You hit the point well on that part, and a part of me feels like this thesis was a bit incomplete in that regard.
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