Hierarchical iterative eigendecomposition for motion segmentation

Antonio Robles-Kelly, Adrian G. Bors, Edwin R. Hancock

Abstract: This paper applies a new clustering approach for identifying and segmenting motion in image sequences. We estimate a matrix whose entries represent similarity probabilities between local motion estimates. We adopt a two step iterative algorithm which consists of a variant of the expectation maximization algorithm for segmenting regions with similar motion. The proposed algorithm updates cluster memberships in one step while it maximizes the expected log-likelihood in the second step. The performance of the algorithm is improved greatly by the use of modal sharpening.