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 gen...

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Bibliographic Details
Published in2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 2; pp. 1132 - 1139 vol. 2
Main Authors Estrada, F.J., Jepson, A.D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Computer science
Continuous improvement
Humans
Image databases
Image segmentation
Partitioning algorithms
Pixel
Robustness
Time measurement
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Summary: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.
ISBN:0769523722
9780769523729
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2005.284