Title :
A PCA-Based Binning Approach for Matching to Large SIFT Database
Author :
Treen, Geoffrey ; Whitehead, Anthony
Author_Institution :
Carleton Univ., Ottawa, ON, Canada
fDate :
May 31 2010-June 2 2010
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.
Keywords :
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; Computer vision; Databases; Robot vision systems; content-based image retrieval; feature extraction; nearest-neighbor search;
Conference_Titel :
Computer and Robot Vision (CRV), 2010 Canadian Conference on
Conference_Location :
Ottawa, ON
Print_ISBN :
978-1-4244-6963-5
