Bregman vantage point trees for efficient nearest neighbor queries

Authors:
Frank Nielsen;Paolo Piro;Michel Barlaud
Affiliations:
École Polytechnique, Palaiseau, France and Sony CSL, Tokyo, Japan;CNRS, University of Nice-Sophia Antipolis, Sophia Antipolis, France;CNRS, University of Nice-Sophia Antipolis, Sophia Antipolis, France
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

Citing 5
Cited 3

Data structures and algorithms for nearest neighbor search in general metric spaces

SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Clustering with Bregman Divergences

The Journal of Machine Learning Research
On Bregman Voronoi diagrams

SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Fast nearest neighbor retrieval for bregman divergences

Proceedings of the 25th international conference on Machine learning
What Is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II

Approximate bregman near neighbors in sublinear time: beyond the triangle inequality

Proceedings of the twenty-eighth annual symposium on Computational geometry
Review: Divergence measures for statistical data processing-An annotated bibliography

Signal Processing
Pattern learning and recognition on statistical manifolds: an information-geometric review

SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Nearest Neighbor (NN) retrieval is a crucial tool of many computer vision tasks. Since the brute-force naive search is too time consuming for most applications, several tailored data structures have been proposed to improve the efficiency of NN search. Among these, vantage point tree (vp-tree) was introduced for information retrieval in metric spaces. Vp-trees have recently shown very good performances for image patch retrieval with respect to the L2 metric. In this paper we generalize the seminal vp-tree construction and search algorithms to the broader class of Bregman divergences. These distorsion measures are preferred in many cases, as they also handle entropic distances (e.g., Kullback-Leibler divergence) besides quadratic distances. We also extend vp-tree to deal with symmetrized Bregman divergences, which are commonplace in applications of content-based multimedia retrieval. We evaluated performances of our Bvp-tree for exact and approximate NN search on two image feature datasets. Our results show good performances of Bvp-tree, specially for symmetrized Bregman NN queries.