DocumentCode :
3226900
Title :
Fast Similarity Search for High-Dimensional Dataset
Author :
Wang, Quan ; You, Suya
Author_Institution :
Comput. Sci. Dept., Univ. of Southern California, Los Angeles, CA
fYear :
2006
fDate :
Dec. 2006
Firstpage :
799
Lastpage :
804
Abstract :
This paper addresses the challenging problem of rapidly searching and matching high-dimensional features for the applications of multimedia database retrieval and pattern recognition. Most current methods suffer from the problem of dimensionality curse. A number of theoretical and experimental studies lead us to pursue a new approach, called fast filtering vector approximation (FFVA) to tackle the problem. FFVA is a nearest neighbor search technique that facilitates rapidly indexing and recovering the most similar matches to a high-dimensional database of features or spatial data. Extensive experiments have demonstrated effectiveness of the proposed approach
Keywords :
database indexing; information filtering; multimedia databases; string matching; FFVA; fast filtering vector approximation; fast similarity search; high-dimensional dataset; indexing; multimedia database retrieval; pattern recognition; spatial data; Filtering; Indexing; Information retrieval; Multimedia databases; Nearest neighbor searches; Noise robustness; Pattern matching; Pattern recognition; Spatial databases; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia, 2006. ISM'06. Eighth IEEE International Symposium on
Conference_Location :
San Diego, CA
Print_ISBN :
0-7695-2746-9
Type :
conf
DOI :
10.1109/ISM.2006.78
Filename :
4061262
Link To Document :
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