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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ID Code: 2980
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:
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Keywords: indexing system, binay search tree
ISBN: 9783540211235
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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