LEADER 02344oam 2200397zu 450 001 9910145698803321 005 20210807003136.0 010 $a1-5090-9068-1 035 $a(CKB)1000000000711115 035 $a(SSID)ssj0000818058 035 $a(PQKBManifestationID)12336610 035 $a(PQKBTitleCode)TC0000818058 035 $a(PQKBWorkID)10831215 035 $a(PQKB)10434260 035 $a(NjHacI)991000000000711115 035 $a(EXLCZ)991000000000711115 100 $a20160829d2007 uy 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$a2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 210 31$a[Place of publication not identified]$cIEEE$d2007 215 $a1 online resource 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a1-4244-1179-3 330 $aLinear 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. 606 $aPattern recognition systems$vCongresses 615 0$aPattern recognition systems 676 $a621.3819598 801 0$bPQKB 906 $aPROCEEDING 912 $a9910145698803321 996 $a2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)$92343557 997 $aUNINA