Spatial Segmentation of Temporal Texture Using Mixture Linear Models

In this paper we propose a novel approach for the spatial segmentation of video sequences containing multiple temporal textures. This work is based on the notion that a single temporal texture can be represented by a lowdimensional linear model. For scenes containing multiple temporal textures, e.g. trees swaying adjacent a flowing river, we extend the single linear model to a mixture of linear models and segment the scene by identifying subspaces within the data using robust generalized principal component analysis (GPCA). Computation is reduced to minutes in Matlab by first identifying models from a sampling of the sequence and using the derived models to segment the remaining data. The effectiveness of our method has been demonstrated in several examples including an application in biomedical image analysis.

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Lee Cooper, Jun Liu, Kun Huang
• Keywords: Computer Vision, ECCV 2006, Linear Models, Multiple Temporal Textures, Temporal Textures