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A Progressive Approach for Similarity Search on Matrix

  • Tsz Nam ChanEmail author
  • Man Lung Yiu
  • Kien A. Hua
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9239)

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.

Keywords

Near Neighbor Query Image Multimedia Database Exact Distance Query Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of ComputingHong Kong Polytechnic UniversityKowloonHong Kong
  2. 2.College of Engineering and Computer ScienceUniversity of Central FloridaOrlandoUSA

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