A Markov Clustering Method for Analyzing Movement Trajectories
Jacob Goldberger, Keren Erez and Moshe Abeles
In: MLSP 2007, 27-29 August 2007, Thessaloniki, Greece.
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.