Readings #1

COMMENTS:
chris aikens

SUMMARY:


This paper first relates sketch recognition to a more common technique gesture recognition. Even though the author considers they are fundamentally different, for some certain situations gesture recognition technique can give good results for sketch recognition problems, therefore it is worthwhile giving some efforts.

And for the rest part of the paper, the author discusses three fundamental gesture recognition methods by, Rubins, Long and Wobbrock

Runbins work (1991) is considered by the author as the first one that applied gesture recognition technique for sketch problem. This method can be simply formulated as 13 features + linear classifiers. Long's work(1996) is an extension of Runbines, while he used 22 features. Wobbrock introduced a so-called $1 recognizer, which is a template matcher rather than a feature based method.



DISCUSSION:

In general, the gesture recognition problem lies in how to build a good classifier, could be based on features or templates. And this usually has two steps, feature extraction, and classifier tanning, after the classifier has been trained, classification is done based on that.

For a good feature, it should be unique, rotation/translation invariant, or hopefully, scaling invariant, how to define a good feature is a key to the problem, Robines and Long have different ways of defining features. These features can be extracted quickly for training data and input data, even though the training for linear classifier might be slow, the classification from linear classifier is fast.

As for the templates, as in Wobbrock's method, instead of finding the invariant features from the first place, the pre-processing (rotation/translation/scaling) is done for every whole single object. And I think that is why this method is slower.

There is no classifier training step, the classifier is just templates (or maybe these templates are trained? not sure, need to take a look at the original paper) from different gesture class, and the classification is simply based on the distance between template and input data, however, those pre-processing (transformations) of input data has already taken a lot of time.

For classifier, no matter linear classifier or neural network, etc, there are quite a lot of well-developed classifiers, so I think this is not a big deal.