A supremum norm based near neighbor search in high dimensional spaces

Authors:
Nikolai Sergeev
Affiliations:
Institute for Neural Information Processing, University of Ulm, Germany
Venue:
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Year:
2012

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Abstract

This paper presents a new near neighbor search. Feature vectors to be stored do not have to be of equal length. Two feature vectors are getting compared with respect to supremum norm. Time demand to learn a new feature vector does not depend on the number of vectors already learned. A query is formulated not as a single feature vector but as a set of features which overcomes the problem of possible permutation of components in a representation vector. Components of a learned feature vector can be cut out - the algorithm is still capable to recognize the remaining part.