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Full-search-equivalent pattern matching with incremental dissimilarity approximations.

Federico Tombari1, Stefano Mattoccia, Luigi Di Stefano

  • 1DEIS, Advanced Research Center on Electronic Systems, University of Bologna, Bologna, Italy. federico.tombari@unibo.it

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 26, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a fast pattern matching technique using Lp norm dissimilarity. It achieves computational savings by employing lower bounds to efficiently prune search candidates, matching Full Search accuracy.

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Area of Science:

  • Computer Science
  • Signal Processing
  • Pattern Recognition

Background:

  • Fast pattern matching is crucial for various computational tasks.
  • Existing methods often face trade-offs between speed and accuracy.
  • Full Search (FS) provides accurate results but is computationally expensive.

Purpose of the Study:

  • To develop a novel, computationally efficient method for fast pattern matching.
  • To ensure the proposed method maintains full-search equivalence in accuracy.
  • To significantly reduce computational cost compared to existing algorithms.

Main Methods:

  • The proposed method utilizes dissimilarity functions derived from the Lp norm (e.g., Sum of Squared Differences (SSD), Sum of Absolute Differences (SAD)).
  • It employs a succession of increasingly tighter lower bounds for these dissimilarity functions.
  • A hierarchy of pruning conditions is established to rapidly eliminate non-matching candidates.

Main Results:

  • The method is proven to be full-search equivalent, guaranteeing accuracy.
  • Experimental comparisons demonstrate remarkable computational efficiency over other full-search equivalent approaches.
  • Significant savings in computation are achieved through effective candidate pruning.

Conclusions:

  • The novel Lp norm-based pattern matching method offers a highly efficient and accurate solution.
  • The proposed bounding and pruning strategy effectively accelerates the search process.
  • This approach represents a significant advancement in fast pattern matching algorithms.