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New benchmark for image segmentation evaluation

[+] Author Affiliations
Feng Ge

Virginia Tech, Department of Electrical and Computer Engineering, Blacksburg, Virginia 24061

Song Wang

University of South Carolina, Department of Computer Science and Engineering, Columbia, South Carolina 29208

Tiecheng Liu

University of South Carolina, Department of Computer Science and Engineering, Columbia, South Carolina 29208

J. Electron. Imaging. 16(3), 033011 (July 27, 2007). doi:10.1117/1.2762250
History: Received August 07, 2006; Revised March 13, 2007; Accepted April 16, 2007; Published July 27, 2007
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Image segmentation and its performance evaluation are very difficult but important problems in computer vision. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and objectivity: For general-purpose segmentation, the ground truth and segmentation accuracy may not be well defined, while embedding the evaluation in a specific application, the evaluation results may not be extensible to other applications. We present a new benchmark to evaluate five different image segmentation methods according to their capability to separate a perceptually salient structure from the background with a relatively small number of segments. This way, we not only find a large variety of images that satisfy the requirement of good generality, but also construct ground-truth segmentations to achieve good objectivity. We also present a special strategy to address two important issues underlying this benchmark: (1) most image-segmentation methods are not developed to directly extract a single salient structure; (2) many real images have multiple salient structures. We apply this benchmark to evaluate and compare the performance of several state-of-the-art image segmentation methods, including the normalized-cut method, the watershed method, the efficient graph-based method, the mean-shift method, and the ratio-cut method.

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© 2007 SPIE and IS&T

Citation

Feng Ge ; Song Wang and Tiecheng Liu
"New benchmark for image segmentation evaluation", J. Electron. Imaging. 16(3), 033011 (July 27, 2007). ; http://dx.doi.org/10.1117/1.2762250


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