Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/70118
Title: A progressive approach for similarity search on matrix
Authors: Chan, TN
Yiu, ML 
Hua, KA
Issue Date: 2015
Publisher: springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2015, v. 9239, p. 373-390 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: We study a similarity search problem on a raw image by its pixel values. We call this problem as matrix similarity search; it has several applications, e.g., object detection, motion estimation, and super-resolution. Given a data image D and a query q, the best match refers to a sub-window of D that is the most similar to q. The state-of-the-art solution applies a sequence of lower bound functions to filter sub-windows and reduce the response time. Unfortunately, it suffers from two drawbacks: (i) its lower bound functions cannot support arbitrary query size, and (ii) it may invoke a large number of lower bound functions, which may incur high cost in the worst-case. In this paper, we propose an efficient solution that overcomes the above drawbacks. First, we present a generic approach to build lower bound functions that are applicable to arbitrary query size and enable trade-offs between bound tightness and computation time. We provide performance guarantee even in the worst-case. Second, to further reduce the number of calls to lower bound functions, we develop a lower bound function for a group of sub-windows. Experimental results on image data demonstrate the efficiency of our proposed methods.
Description: 14th International Symposium on Advances in Spatial and Temporal Database, SSTD 2015, Hong Kong, China, August 26-28, 2015
URI: http://hdl.handle.net/10397/70118
ISBN: 978-3-319-22362-9 (print)
978-3-319-22363-6 (electronic)
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-22363-6_20
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