The Community for Technology Leaders
Green Image
Issue No. 04 - April (2017 vol. 29)
ISSN: 1041-4347
pp: 784-797
Tsz Nam Chan , Department of Computing, Hong Kong Polytechnic University, Hong Kong, Hung Hom, Kowloon
Man Lung Yiu , Department of Computing, Hong Kong Polytechnic University, Hong Kong, Hung Hom, Kowloon
Kien A. Hua , College of Engineering & Computer Science, University of Central Florida, Orlando, FL
ABSTRACT
We study a nearest neighbor search problem on a matrix by its element values. Given a data matrix $_$D$_$ and a query matrix $_$q$_$ , the sub-window nearest neighbor search problem finds a sub-window of $_$D$_$ that is the most similar to $_$q$_$ . This problem has a wide range of applications, e.g., geospatial data integration, object detection, and motion estimation. In this paper, we propose an efficient progressive search solution that overcomes the drawbacks of existing solutions. First, we present a generic approach to build level-based lower bound functions on top of basic lower bound functions. Second, we develop a novel lower bound function for a group of sub-windows, in order to boost the efficiency of our solution. Furthermore, we extend our solution to support irregular-shaped queries. Experimental results on real data demonstrate the efficiency of our proposed methods.
INDEX TERMS
Search problems, Upper bound, Nearest neighbor searches, Satellites, Clouds, Shape, Junctions
CITATION

T. N. Chan, M. L. Yiu and K. A. Hua, "Efficient Sub-Window Nearest Neighbor Search on Matrix," in IEEE Transactions on Knowledge & Data Engineering, vol. 29, no. 4, pp. 784-797, 2017.
doi:10.1109/TKDE.2016.2633357
304 ms
(Ver 3.3 (11022016))