Paper Info
Title
Fast Neighborhood Graph Search Using Cartesian Concatenation
Abstract
In this paper, we propose a new data structure for approximate nearest neighbor search. This structure augments the neighborhood graph with a bridge graph. We propose to exploit Cartesian concatenation to produce a large set of vectors, called bridge vectors, from several small sets of sub vectors. Each bridge vector is connected with a few reference vectors near to it, forming a bridge graph. Our approach finds nearest neighbors by simultaneously traversing the neighborhood graph and the bridge graph in the best-first strategy. The success of our approach stems from two factors: the exact nearest neighbor search over a large number of bridge vectors can be done quickly, and the reference vectors connected to a bridge (reference) vector near the query are also likely to be near the query. Experimental results on searching over large scale datasets (SIFT, GIST and HOG) show that our approach outperforms state-of-the-art ANN search algorithms in terms of efficiency and accuracy. The combination of our approach with the IVFADC system~JegouDS11 also shows superior performance over the BIGANN dataset of 1 billion SIFT features compared with the best previously published result.
Year
DOI
Venue
2013
10.1109/ICCV.2013.265
international conference on computer vision
Keywords
DocType
Volume
fast neighborhood graph search,cartesian concatenation,bridge graph,large scale datasets,approximate nearest neighbor search,large set,exact nearest neighbor search,bridge vector,large number,reference vector,nearest neighbor,neighborhood graph,graph theory,learning artificial intelligence,data structures,approximation theory
Conference
abs/1312.3062
Issue
ISSN
Citations 
1
1550-5499
10
PageRank 
References 
Authors
0.55
40
6
Name
Order
Citations
PageRank
Jing Wang1100.55
Jingdong Wang24198156.76
Gang Zeng394970.21
Rui Gan418313.62
Shipeng Li53902252.94
Baining Guo63970194.91