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CVPR
2005
IEEE

Quantitative Evaluation of a Novel Image Segmentation Algorithm

15 years 1 months ago
Quantitative Evaluation of a Novel Image Segmentation Algorithm
We present a quantitative evaluation of SE-MinCut, a novel segmentation algorithm based on spectral embedding and minimum cut. We use human segmentations from the Berkeley Segmentation Database as ground truth and propose suitable measures to evaluate segmentation quality. With these measures we generate precision/recall curves for SE-MinCut and three of the leading segmentation algorithms: Mean-Shift, Normalized Cuts, and the Local Variation algorithm. These curves characterize the performance of each algorithm over a range of input parameters. We compare the precision/recall curves for the four algorithms and show segmented images that support the conclusions obtained from the quantitative evaluation.
Francisco J. Estrada, Allan D. Jepson
Added 12 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 2005
Where CVPR
Authors Francisco J. Estrada, Allan D. Jepson
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