Issue No. 04 - April (2017 vol. 29)
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
We study a nearest neighbor search problem on a matrix by its element values. Given a data matrix
and a query matrix $_$D$_$ , the sub-window nearest neighbor search problem finds a sub-window of $_$q$_$ that is the most similar to $_$D$_$ . 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. $_$q$_$
Search problems, Upper bound, Nearest neighbor searches, Satellites, Clouds, Shape, Junctions
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.