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Optimal Feature Selection for Subspace Image Matching

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Optimal Feature Selection for Subspace Image Matching
Image matching has been a central research topic in computer vision over the last decades. Typical approaches for correspondence involve matching features between images. In this paper, we present a novel problem for establishing correspondences between a sparse set of image features and a previously learned subspace model. We formulate the matching task as an energy minimization, and jointly optimize over all possible feature assignments and parameters of the subspace model. This problem is in general NP-hard. We propose a convex relaxation approximation, and develop two optimization strategies: naive gradient-descent and quadratic programming. Alternatively, we reformulate the optimization criterion as a sparse eigenvalue problem, and solve it using a recently proposed backward greedy algorithm. Experimental results on facial feature detection show that the quadratic programming solution provides better selection mechanism for relevant features.
Gemma Roig, Xavier Boix, Fernando De la Torre
Added 31 Mar 2010
Updated 06 Jan 2011
Type Conference
Year 2009
Where Workshop ICCV
Authors Gemma Roig, Xavier Boix, Fernando De la Torre
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