Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Algorithms for Fast Vector Quantization Department of Computer Science
 

Summary: Algorithms for Fast Vector Quantization
Sunil Arya
Department of Computer Science
The Hong Kong University of Science and Technology
Clear Water Bay, Kowloon, Hong Kong
David M. Mount
Department of Computer Science and
Institute for Advanced Computer Studies
University of Maryland, College Park, Maryland, USA
Abstract
Nearest neighbor searching is an important geometric subproblem in vector quanti-
zation. Existing studies have shown that the difficulty of solving this problem efficiently
grows rapidly with dimension. Indeed, existing approaches on unstructured codebooks
in dimension 16 are little better than brute-force search. We show that if one is willing
to relax the requirement of finding the true nearest neighbor then dramatic improve-
ments in running time are possible, with negligible degradation in the quality of the
result. We present an empirical study of three nearest neighbor algorithms on a number
of data distributions, and in dimensions varying from 8 to 16. The first algorithm is
the standard k-d tree algorithm which has been enhanced to use incremental distance
calculation, the second is a further improvement that orders search by the proximity of

  

Source: Arya, Sunil - Department of Computer Science, Hong Kong University of Science and Technology
Mount, David - Institute for Advanced Computer Studies & Department of Computer Science, University of Maryland at College Park

 

Collections: Computer Technologies and Information Sciences