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Higher-Order Grouping on the Grassmann Manifold

University of Grenoble, France
Date : 15/11/2013



The higher-order clustering problem arises when data is drawn from multiple subspaces or when observations fit a higher-order parametric model. In my talk I will present a tensor decomposition solution to this problem and its refinement based on estimation on a Grassmann manifold. This method exploits recent advances in online estimation on the Grassmann manifold and is resultantly efficient and scalable with a low memory requirement. I will present results of this method applied to a variety of segmentation problems including planar segmentation of Kinect depth maps and motion segmentation of the Hopkins 155 dataset for which we achieve performance comparable to the state-of-the-art.

Brief Bio:

Venu Madhav Govindu is with the Department of Electrical Engineering, Indian Institute of Science, Bengaluru. His research interests pertain to geometric and statistical estimation in computer vision.