2002
@inproceedings{Vol2002,
vgclass = {refpap},
vgproject = {cbir},
author = {Stephan Volmer},
title = {Fast Approximate Nearest-Neighbor Queries in Metric
Feature Spaces by Buoy Indexing},
editor = {Shi-Kuo Chang and Zen Chen and Suh-Yin Lee},
booktitle = {Proceedings of the 5th International Conference on Recent
Advances in Visual Information Systems (VISUAL 2002)},
address = {Hsin Chu, Taiwan},
number = {2314},
series = {Lecture Notes in Computer Science},
pages = {36--49},
publisher = {Springer-Verlag},
month = {March~11--13},
year = {2002},
url = {http://www.springerlink.com/link.asp?id=0k76hk9t00hlxch6},
abstract = {An indexing scheme for solving the problem of nearest
neighbor queries in generic metric feature spaces for content-based
retrieval is proposed aiming to break the "dimensionality curse." The
basis for the proposed method is the partitioning of the feature
dataset into a fixed number of clusters that are represented by single
buoys. Upon submission of a query request, only a small number of
clusters whose buoys are close to the query object are considered for
the approximate query result, cutting down the amount of data to be
processed effectively. Results from extensive experimentation
concerning the retrieval accuracy are given. The influence of control
parameters is investigated with respect to the tradeoff between
retrieval accuracy and query execution time.},
}