Authors:
Faraj Alhwarin
;
Alexander Ferrein
and
Ingrid Scholl
Affiliation:
FH Aachen University of Applied Sciences, Germany
Keyword(s):
Feature Matching, Hash Tree, Fast NN Search.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Clustering
;
Computer Vision, Visualization and Computer Graphics
;
Data Engineering
;
Image Understanding
;
Information Retrieval
;
Ontologies and the Semantic Web
;
Pattern Recognition
;
Software Engineering
;
Theory and Methods
Abstract:
Nearest Neighbour (NN) search is an essential and important problem in many areas, including multimedia
databases, data mining and computer vision. For low-dimensional spaces a variety of tree-based NN search
algorithms efficiently cope with finding the NN, for high-dimensional spaces, however, these methods are inefficient.
Even for Locality Sensitive Hashing (LSH) methods which solve the task approximately by grouping
sample points that are nearby in the search space into buckets, it is difficult to find the right parameters. In
this paper, we propose a novel hashing method that ensures a high probability of NNs being located in the
same hash buckets and a balanced distribution of data across all the buckets. The proposed method is based on
computing a selected number of pairwise uncorrelated and uniformly-distributed Circular Random Variables
(CRVs) from the sample points. The method has been tested on a large dataset of SIFT features and was
compared to LSH and the Fast Library f
or Approximated NN search (FLANN) matcher with linear search
as the base line. The experimental results show that our method significantly reduces the search query time
while preserving the search quality, in particular for dynamic databases and small databases whose size does
not exceed 200k points.
(More)