Summary
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 component in any vector quantization system. The cost of this higher speed (compared to tabular indexing) is a negligible degradation of the distortion error. Nevertheless, a parameter allows the user to tradeoff speed for a lower distortion error. A distinctive and attractive feature of this tree indexing system is that it can follow non-stationary codebooks by performing local repairs to its indexing tree. Experimental results show that this indexing system is very fast; it outperforms similar tree indexing systems like TSVQ and K-trees.
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Maire, F., Bader, S., Wathne, F. (2004). Fast Indexing of Codebook Vectors Using Dynamic Binary Search Trees With Fat Decision Hyperplanes. In: Rajapakse, J.C., Wang, L. (eds) Neural Information Processing: Research and Development. Studies in Fuzziness and Soft Computing, vol 152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39935-3_9
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DOI: https://doi.org/10.1007/978-3-540-39935-3_9
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