Hierarchical iterative eigendecomposition for motion segmentation

Author

Robles-Kelly, A. ; Bors, A.G. ; Hancock, E.R.

Author_Institution

Dept. of Comput. Sci., York Univ., UK

Volume

2

fYear

2001

Firstpage

363

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

Keywords

eigenvalues and eigenfunctions; image motion analysis; image segmentation; image sequences; iterative methods; matrix decomposition; maximum likelihood estimation; motion estimation; Bernoulli distributions; cluster memberships; clustering approach; expectation-maximization algorithm; hierarchical iterative eigendecomposition; image sequences; log-likelihood maximization; matrix factorization; maximum likelihood framework; modal sharpening; motion segmentation; regions segmentation; similarity probabilities; two step iterative algorithm; Clustering algorithms; Coherence; Computer vision; Graph theory; Image segmentation; Image sequences; Iterative algorithms; Motion estimation; Motion segmentation; Object detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2001. Proceedings. 2001 International Conference on

Conference_Location

Thessaloniki

Print_ISBN

0-7803-6725-1

Type

conf

DOI

10.1109/ICIP.2001.958503

Filename

958503