Selecting a Small Set of Optimal Gestures from an Extensive Lexicon

TL;DR

This paper introduces an objective function and an efficient algorithm for selecting an optimal subset of gestures from a large lexicon, balancing recognition performance and user preferences.

Contribution

It presents the ellipsoidal distance ratio metric (EDRM) and a numerical method to incorporate subjective preferences into gesture selection.

Findings

Efficient algorithm for selecting top gestures from large lexicons.

Incorporation of subjective preferences into gesture selection.

Demonstrated improvement in gesture recognition optimization.

Abstract

Finding the best set of gestures to use for a given computer recognition problem is an essential part of optimizing the recognition performance while being mindful to those who may articulate the gestures. An objective function, called the ellipsoidal distance ratio metric (EDRM), for determining the best gestures from a larger lexicon library is presented, along with a numerical method for incorporating subjective preferences. In particular, we demonstrate an efficient algorithm that chooses the best $n$ gestures from a lexicon of $m$ gestures where typically $n \ll m$ using a weighting of both subjective and objective measures.