Abstract
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 low-dimensional 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.
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Cooper, L., Liu, J., Huang, K. (2007). Spatial Segmentation of Temporal Texture Using Mixture Linear Models. In: Vidal, R., Heyden, A., Ma, Y. (eds) Dynamical Vision. WDV WDV 2006 2005. Lecture Notes in Computer Science, vol 4358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70932-9_11
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DOI: https://doi.org/10.1007/978-3-540-70932-9_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70931-2
Online ISBN: 978-3-540-70932-9
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