Paper Info
Title
A PCA-Based Binning Approach for Matching to Large SIFT Database
Abstract
A method for efficiently finding SIFT correspondences in large keypoint archives by separating a database into bins – using the principal components of the SIFT descriptor vector as the binning criteria – is proposed. This technique builds upon our previous efforts to improve SIFT matching speed in image pairs, and will find correspondences approximately three times faster than FLANN – the approximate nearest-neighbor search library that implements the existing state of the art – for the same recall-precision performance.
Year
DOI
Venue
2010
10.1109/CRV.2010.9
CRV
Keywords
Field
DocType
content-based retrieval,image retrieval,principal component analysis,visual databases,FLANN,PCA-based binning approach,SIFT descriptor vector,approximate nearest-neighbor search library,large SIFT database,recall-precision performance,content-based image retrieval,feature extraction,nearest-neighbor search
Scale-invariant feature transform,Computer vision,Pattern recognition,Computer science,Image retrieval,Feature extraction,Content based retrieval,Artificial intelligence,Database,Content-based image retrieval,Nearest neighbor search,Principal component analysis
Conference
Citations 
PageRank 
References 
2
0.37
7
Authors
2
Name
Order
Citations
PageRank
Geoffrey Treen120.37
Anthony Whitehead214320.84