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Efficient high-dimensional indexing by sorting principal component
- Source :
-
Pattern Recognition Letters . Dec2007, Vol. 28 Issue 16, p2412-2418. 7p. - Publication Year :
- 2007
-
Abstract
- Abstract: The vector approximation file (VA-file) approach is an efficient high-dimensional indexing method for image retrieval in large database. Some extensions of VA-file have been proposed towards better query performance. However, all of these methods applying sequential scan need read the whole vector approximation file. In this paper, we present a new indexing structure based on vector approximation method, in which only a small part of approximation file need be accessed. First, principal component analysis is used to map multidimensional points to a 1D line. Then a B +-tree is built to index the approximate vector according to principal component. When performing k-nearest neighbor search, the partial distortion searching algorithm is used to reject the improper approximate vectors. Only a small set of approximate vectors need to be sequentially scanned during the search, which can reduce the CPU cost and I/O cost dramatically. Experiment results on large image databases show that the new approach provides a faster search speed than the other VA-file approaches. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 01678655
- Volume :
- 28
- Issue :
- 16
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition Letters
- Publication Type :
- Academic Journal
- Accession number :
- 27049706
- Full Text :
- https://doi.org/10.1016/j.patrec.2007.08.007