Reading #2 Rubine’s Linear Recognizer

Reading #2 Rubine’s Linear Recognizer

Comments on:
Amir

Summary: 

In this paper (in 1991), Rubine introduces GRANDMA as  a toolkit to easy the programmer of gestural interface. He uses GDP as an example of gesture based application. It defines some gestures and their related operations, using GRANDMA to recognize the input and then perform specified manipulation on graph. GRANDMA can only recognize single-stroke gestures.

Then, Rubine explains his gesture recognizer. His recognizer has two steps: first, calculate some features of the stroke. Then, using a linear machine, the stroke is classified as the class which returns the maximum value.

The author wants these features:

(1) can be computed in O(1) per input;

(2) to be meaningful, Among the 13 features chosen subjectively (or empirically) by Rubine, the first 11 are about the geometry character of a stroke and the last two are about speed.

(3) of course, be able to differentiate strokes.

A linear evaluation is computed to classify gestures. It simple adds all the features multiplied with different weights to give a value which can represent the fitness of being certain class.

The weights’ assignment is the critical part of the classification. They are obtained from training data as:

Rubine also introduce two ways to reject a classification result:

And Mahalanobis distance:

Rubine also discuss the tradeoff between rejection and undo in the gesture application.

Discussion:

Compare with $1 dollar, Rubine’s recognizer is more efficient after the training. It has limitation in the case that not enough data is offered (as Rubine’s analysis, 15 samples are needed to give a good accuracy).  Another one is it can only deal with single-stroke gesture.

The interesting thing is Rubine mention multi-finger input at the end of his paper. Does it has any relation with the Jeff Han’s multi-touch screen  technologies?