An evolutionary approach for learning motion class patterns

authored by

Meinard Müller, Bastian Demuth, Bodo Rosenhahn

Abstract

This article presents a genetic learning algorithm to derive discrete patterns that can be used for classification and retrieval of 3D motion capture data. Based on boolean motion features, the idea is to learn motion class patterns in an evolutionary process with the objective to discriminate a given set of positive from a given set of negative training motions. Here, the fitness of a pattern is measured with respect to precision and recall in a retrieval scenario, where the pattern is used as a motion query. Our experiments show that motion class patterns can automate query specification without loss of retrieval quality.

External Organisation(s)

Max-Planck Institute for Informatics
University of Bonn

Type

Conference contribution

Pages

365-374

No. of pages

10

Publication date

2008

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Theoretical Computer Science, General Computer Science

Electronic version(s)

https://doi.org/10.1007/978-3-540-69321-5_37 (Access: Unknown)