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Performance Evaluation of State-of-the-Art Discrete Symmetry Detection Algorithms
M. Park, S. Lee, P. Chen, S. Kashyap, A.A. Butt, and Y. Liu
Proceedings of CVPR 2008, June, 2008.

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Symmetry is one of the important cues for human and machine perception of the world. For over three decades, automatic symmetry detection from images/patterns has been a standing topic in computer vision. We present a timely, systematic, and quantitative performance evaluation of three state of the art discrete symmetry detection algo- rithms. This evaluation scheme includes a set of carefully chosen synthetic and real images presenting justified, unambiguous single or multiple dominant symmetries, and a pair of well-defined success rates for validation. We make our 176 test images with associated hand-labeled ground truth publicly available with this paper. In addition, we explore the potential contribution of symmetry detection for object recognition by testing the symmetry detection algorithm on three publicly available object recognition image sets (PASCAL VOC'07, MSRC and Caltech-256). Our results indicate that even after several decades of effort, symmetry detection in real-world images remains a challenging, unsolved problem in computer vision. Meanwhile, we illustrate its future potential in object recognition.

Associated center: VASC

Note: (to appear)

M. Park, S. Lee, P. Chen, S. Kashyap, A.A. Butt, and Y. Liu, "Performance Evaluation of State-of-the-Art Discrete Symmetry Detection Algorithms," Proceedings of CVPR 2008, June, 2008.

@inproceedings{Park_2008_6044,
   author = "Minwoo Park and Seungkyu Lee and Po-Chun Chen and Somesh Kashyap and Asad A. Butt and Yanxi Liu",
   title = "Performance Evaluation of State-of-the-Art Discrete Symmetry Detection Algorithms",
   booktitle = "Proceedings of CVPR 2008",
   month = "June",
   year = "2008",
   note = "(to appear)"
}