Region-based image segmentation evaluation via perceptual pooling strategies

Region-based image segmentation evaluation via perceptual pooling strategies Image segmentation is an essential step for many computer vision tasks. Evaluating the quality of image segmentations becomes indispensable for choosing an appropriate output of the image segmentation algorithms. To quantitatively evaluate the segmentation quality, various evaluation measures have been proposed to produce a quality map, and a spatial pooling algorithm is followed to combine the quality map into a single quality score. In this paper, we propose two pooling strategies instead of using the conventional spatial average operation. By assigning perceptual meaningful weights to the quality maps, we obtain evaluation measures that are correlated with the human perception of segmentation quality. Specifically, a quality-based and a visual importance-based pooling strategies are designed and tested on some popular evaluation measures, respectively. To the best of our knowledge, this is the first work that applies perceptual pooling strategies for segmentation evaluation. Extensive experiments are conducted on a subjective evaluation benchmark and the Berkeley Segmentation Dataset (BSDS500). The results indicate that the proposed strategies can improve the performance of existing evaluation measures and produce a more perceptually meaningful judgment on the segmentation quality. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Machine Vision and Applications Springer Journals

Region-based image segmentation evaluation via perceptual pooling strategies

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Computer Science; Pattern Recognition; Image Processing and Computer Vision; Communications Engineering, Networks
ISSN
0932-8092
eISSN
1432-1769
D.O.I.
10.1007/s00138-017-0903-x
Publisher site
See Article on Publisher Site

Abstract

Image segmentation is an essential step for many computer vision tasks. Evaluating the quality of image segmentations becomes indispensable for choosing an appropriate output of the image segmentation algorithms. To quantitatively evaluate the segmentation quality, various evaluation measures have been proposed to produce a quality map, and a spatial pooling algorithm is followed to combine the quality map into a single quality score. In this paper, we propose two pooling strategies instead of using the conventional spatial average operation. By assigning perceptual meaningful weights to the quality maps, we obtain evaluation measures that are correlated with the human perception of segmentation quality. Specifically, a quality-based and a visual importance-based pooling strategies are designed and tested on some popular evaluation measures, respectively. To the best of our knowledge, this is the first work that applies perceptual pooling strategies for segmentation evaluation. Extensive experiments are conducted on a subjective evaluation benchmark and the Berkeley Segmentation Dataset (BSDS500). The results indicate that the proposed strategies can improve the performance of existing evaluation measures and produce a more perceptually meaningful judgment on the segmentation quality.

Journal

Machine Vision and ApplicationsSpringer Journals

Published: Jan 8, 2018

References

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