Comment:
Yue Li
Summary:
For personalized, gesture-based interaction, it is hard to foresee what gestures end-users will specify. And end-users always want to provide only a few samples. That is the reason why Protractor, a template-based recognizer, was designed.
Preprocess is to remove irrelevant factors. Resample and translation are for drawing speed and location, and rotation are for reducing noise in orientation.
Classification is to calculate optimal angular distances, which measure the angle between two samples in the space. To be robust, protractor always rotates templates with a extra angle. And use close-form solution to find the minimum angular distance.
Due to the close-form solution, protractor is faster than $1, which searches the minimum distance step by step.
Discussion:
Protractor, though similar to $1, is faster. It employs close-form solution instead of search step by step. However, error rates are always as same as $1. Protractor has advantages in dealing with orientation-sensitive application. So its dataset can include orientation-sensitive samples, which enlarge the dataset of $1. Actually, a good idea is Protractor.
Metric of distance is very important in classification. A good metric can give high accuracy and save much time, like Protractor. However, it seems that there is no detailed method to find which metric is the best, except trying one by one.
Template-based method is always fast, but cannot deal with some unknown gestures.
While, parametric method is always slow, but can deal with some unknown gestures, due to its ability to module the distribution of samples.
I think protractor deals with orientation problem better than how $n does. We can also see that how to process the original stroke is very important for template-matching method. That is why protractor introduce optimal rotation. And what is the meaning of module the distribution of samples?
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