An evolutionary approach for learning motion class patterns

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. © 2008 Springer-Verlag Berlin Heidelberg.