Fast Indexing of Codebook Vectors Using Dynamic Binary Search Trees with Fat Decision Hyperplanes
Maire, Frederic D., Bader, Sebastian, & Wathne, Frank (2004) Fast Indexing of Codebook Vectors Using Dynamic Binary Search Trees with Fat Decision Hyperplanes. In Jagath, Rajapakse & Wang, Lipo (Eds.) Neural Information Processing: Research and Development, Series: Studies in Fuzziness and Soft Computing. Springer, Berlin ; New York, pp. 150-166.
We describe a new indexing tree system for high dimensional codebook vectors. This indexing system uses a dynamic binary search tree with fat decision hyperplanes. The system is generic, adaptive and can be used as a software com ponent in any vector quantization system. The cost of this higher speed (compared to tabular indexing) is a negligible degradation of the distortion error. Neverthe less, a parameter allows the user to tradeo# speed for a lower distortion error. A distinctive and attractive feature of this tree indexing system is that it can follow nonstationary codebooks by performing local repairs to its indexing tree. Exper imental results show that this indexing system is very fast; it outperforms similar tree indexing systems like TSVQ and Ktrees.
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|Item Type:||Book Chapter|
|Additional Information:||For more information about this book please refer to the publisher's website (link above) or contact the author: email@example.com|
|Keywords:||indexing system, binay search tree|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2004 Springer|
|Deposited On:||19 Jul 2006 00:00|
|Last Modified:||29 Feb 2012 13:05|
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