TY - JOUR
T1 - Automatic peak number detection in image symmetry analysis
AU - He, Jingrui
AU - Li, Mingjing
AU - Zhang, Hong Jiang
AU - Tong, Hanghang
AU - Zhang, Changshui
N1 - Funding Information:
Acknowledgements. This work was supported by National High Technology Research and Development Program of China (863 Program) under contract No.2001AA114190.
PY - 2004
Y1 - 2004
N2 - 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.
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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U2 - 10.1007/978-3-540-30543-9_15
DO - 10.1007/978-3-540-30543-9_15
M3 - Article
AN - SCOPUS:35048867876
SN - 0302-9743
VL - 3333
SP - 111
EP - 118
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -