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Published June 2007 | Published
Book Section - Chapter Open

Approximate Nearest Subspace Search with Applications to Pattern Recognition

Abstract

Linear and affine subspaces are commonly used to describe appearance of objects under different lighting, viewpoint, articulation, and identity. A natural problem arising from their use is - given a query image portion represented as a point in some high dimensional space - find a subspace near to the query. This paper presents an efficient solution to the approximate nearest subspace problem for both linear and affine subspaces. Our method is based on a simple reduction to the problem of nearest point search, and can thus employ tree based search or locality sensitive hashing to find a near subspace. Further speedup may be achieved by using random projections to lower the dimensionality of the problem. We provide theoretical proofs of correctness and error bounds of our construction and demonstrate its capabilities on synthetic and real data. Our experiments demonstrate that an approximate nearest subspace can be located significantly faster than the exact nearest subspace, while at the same time it can find better matches compared to a similar search on points, in the presence of variations due to viewpoint, lighting etc.

Additional Information

© 2007 IEEE. R.B and T.H. were supported in part by the A.M.N. Fund for the promotion of science, culture and arts in Israel and by the European Community grant IST-2002-506766 Aim@Shape. The vision group at the Weizmann Institute is supported in part by the Moross Foundation. L.Z.-M. was supported by ONR grant N00014-06-1-0734 and NSF grant CMS-0428075. Author names are ordered alphabetically due to equal contribution.

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August 19, 2023
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