BMVC 2004, Kingston, 7th-9th Sept, 2004
EM Clustering of Incomplete Data Applied to Motion Segmentation
K.Y. Wong, L. Ye and M.E. Spetsakis(York University, Canada)
Many clustering problems in Computer Vision group data points that are the result of
statistical estimation and these data points can have a great amount of uncertainty.
Motion segmentation by clustering of optical flow is such an example because very
often optical flow cannot be estimated without significant uncertainty. We present a EM
based clustering algorithm for incomplete data and we apply it to the problem of motion
segmentation. The input to the algorithm are the velocity likelihoods and the number of
clusters. The algorithm is mathematically very elegant because it does not impose any
constraints on the velocity likelihood thus multi-modal likelihood is modeled without
difficulty. Coupled with a sophisticated correlated image noise model, the algorithm can
handle substantial deviations from the intensity constancy assumption. Experiments
with real image sequences show excellent results.