A Markov clustering method for analyzing movement trajectories

Jacob Goldberger, Keren Erez, Moshe Abeles

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

In this study we analyze monkeys' hand movement; our strategy is compositional, division of complex movement into basic simple components-primitives. Representing each trajectory segment as vectors of directions, we model the movement trajectory as a large Markov process where each state is related with an average trajectory pattern. In the next step, in order to find the movements primitives, we cluster the Markov states according to their probabilistic similarity. We present an information theoretic co-clustering algorithm which can be interpreted as a block-matrix approximation of the Markov transition matrix. The performance of the suggested approach is demonstrated on real recorded data.

Original languageEnglish
Title of host publicationMachine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP
Pages211-216
Number of pages6
DOIs
StatePublished - 2007
Event17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007 - Thessaloniki, Greece
Duration: 27 Aug 200729 Aug 2007

Publication series

NameMachine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP

Conference

Conference17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007
Country/TerritoryGreece
CityThessaloniki
Period27/08/0729/08/07

Fingerprint

Dive into the research topics of 'A Markov clustering method for analyzing movement trajectories'. Together they form a unique fingerprint.

Cite this