Visual Similarity of Pen Gestures

Visual Similarity of Pen Gestures:

Introduction:

         Supporting Pen Gestures is a desirable feature for user interface because it is fast by means of specifying both operands and operation in one stroke. However, it is difficult to design excellent gestures. Sometimes, gestures are hard to be remembered by users. Sometimes, they are misrecognized by computers. The primary contribution of this paper is to provide 22 possible predictors for similarity of features obtained through gesture similarity experiments.

What are excellent features:

Similar operations with a clear spatial mapping, such as scroll up and scroll down, should be assigned similar gestures. Conversely, gestures for more abstract operations that are similar, such as cut and paste, may be easily confused if they are visually similar.

Similarity Trial One: 

The data set of trial one consists of gestures that are varied widely in terms of how people perceive them.

The purpose of trial one is

(1)to determine what measurable geometric properties of the gestures influenced their perceived similarity

(2)to produce a model of gesture similarity. When given two gestures, the model could predict how similar people would perceive those two gestures to be

After analysis, the following 22 possible predictors for similarity are given,

As we can see from table 2, for this widely varied data set in trial one, Curviness and Total Angle Traversed/Total length is most important in determine the similarity of two gestures.

Similarity Trial Two:

The purpose of trial two is exploring how systematically varying different types of features would affect perceived similarity.

The data sets used in trial two are as follows,

After analyzing the result of trial two, the authors conclude that Log( aspect ) and density to be the main factors in determine the similarity of two gestures.

Bibliography:

Long A C, Jr, Landay J A, etal. Visual Similarity of Pen Gestures[D]. Berkeley, California:Department of Electrical Engineering and Computer Science, University of California at Berkeley, April, 2000.