Title :
Maximum likelihood motion segmentation using eigendecomposition
Author :
Robles-Kelly, A. ; Hancock, E.R.
Author_Institution :
Dept. of Comput. Sci., York Univ., UK
Abstract :
This paper presents an iterative maximum likelihood framework for motion segmentation. Our representation of the segmentation problem is based on a similarity matrix for the motion vectors for pairs of pixel blocks. By applying eigendecomposition to the similarity matrix, we develop a maximum likelihood method for grouping the pixel blocks into objects which share a common motion vector. We experiment with the resulting clustering method on a number of real-world motion sequences. Here ground truth data indicates that the method can result in motion classification errors as low as 3%
Keywords :
eigenvalues and eigenfunctions; image classification; image segmentation; image sequences; iterative methods; matrix decomposition; maximum likelihood estimation; motion estimation; pattern clustering; clustering method; eigendecomposition; iterative framework; maximum likelihood method; motion classification; motion segmentation; motion vectors; pixel blocks; real-world motion sequences; similarity matrix; Clustering algorithms; Clustering methods; Computer science; Computer vision; Maximum likelihood detection; Maximum likelihood estimation; Motion control; Motion estimation; Motion segmentation; Object detection;
Conference_Titel :
Image Analysis and Processing, 2001. Proceedings. 11th International Conference on
Conference_Location :
Palermo
Print_ISBN :
0-7695-1183-X
DOI :
10.1109/ICIAP.2001.956986
