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An analysis of multi-level filtering for high dimensional image data

University of British Columbia

Image database systems are very useful in many applications. To design an effective image database system, high dimensional image feature vectors have to be extracted from the images automatically. Each comparison between them tends to be expensive, so sequential comparisons are usually impractical. Moreover, the. traditional multi-dimensional indexing structures are incapable of handling these high-dimensional vectors efficiently. Thus, it has been proposed to abstract lower dimensional k-D vector from the original N-D feature vector, where k

Thesis/Dissertation

application/pdf

2009-02-11

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http://hdl.handle.net/2429/4435

Computer Science

University of British Columbia

UBCV

DSpace