Automatic peak number detection in image symmetry analysis

Jingrui He, Mingjing Li, Hong Jiang Zhang, Hanghang Tong, Changshui Zhang

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

In repeated pattern analysis, peak number detection in autocorrelation is of key importance, which subsequently determines the correctness of the constructed lattice. Previous work inevitably needs users to select peak number manually, which limits its generalization to applications in large image database. The main contribution of this paper is to propose an optimization-based approach for automatic peak number detection, i.e., we first formulate it as an optimization problem by a straightforward yet effective criterion function, and then resort to Simulated Annealing to optimize it. Based on this approach, we design a new feature to depict image symmetry property which can be automatically extracted for repeated pattern retrieval. Experimental results demonstrate the effectiveness of the optimization approach and the superiority of symmetry feature over wavelet feature in discriminating similar repeated patterns.

Original languageEnglish (US)
Pages (from-to)111-118
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3333
StatePublished - 2004
Externally publishedYes

Fingerprint

Databases
Symmetry
Pattern Analysis
Optimization
Image Database
Simulated annealing
Autocorrelation
Simulated Annealing
Correctness
Wavelets
Retrieval
Optimise
Optimization Problem
Experimental Results
Demonstrate
Generalization
Design

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

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abstract = "In repeated pattern analysis, peak number detection in autocorrelation is of key importance, which subsequently determines the correctness of the constructed lattice. Previous work inevitably needs users to select peak number manually, which limits its generalization to applications in large image database. The main contribution of this paper is to propose an optimization-based approach for automatic peak number detection, i.e., we first formulate it as an optimization problem by a straightforward yet effective criterion function, and then resort to Simulated Annealing to optimize it. Based on this approach, we design a new feature to depict image symmetry property which can be automatically extracted for repeated pattern retrieval. Experimental results demonstrate the effectiveness of the optimization approach and the superiority of symmetry feature over wavelet feature in discriminating similar repeated patterns.",
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AU - He, Jingrui

AU - Li, Mingjing

AU - Zhang, Hong Jiang

AU - Tong, Hanghang

AU - Zhang, Changshui

PY - 2004

Y1 - 2004

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AB - In repeated pattern analysis, peak number detection in autocorrelation is of key importance, which subsequently determines the correctness of the constructed lattice. Previous work inevitably needs users to select peak number manually, which limits its generalization to applications in large image database. The main contribution of this paper is to propose an optimization-based approach for automatic peak number detection, i.e., we first formulate it as an optimization problem by a straightforward yet effective criterion function, and then resort to Simulated Annealing to optimize it. Based on this approach, we design a new feature to depict image symmetry property which can be automatically extracted for repeated pattern retrieval. Experimental results demonstrate the effectiveness of the optimization approach and the superiority of symmetry feature over wavelet feature in discriminating similar repeated patterns.

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