We present a hybrid parametric and nonparametric algorithm, exemplar cut, for generating class-specific object segmentation hypotheses. For the parametric part, we train a pylon model on a hierarchical region tree as the energy function for segmentation. For the nonparametric part, we match the input image with each exemplar by using regions to obtain a score which augments the energy function from the pylon model. Our method thus generates a set of highly plausible segmentation hypotheses by solving a series of exemplar augmented graph cuts. Experimental results on the Graz and PASCAL datasets show that the proposed algorithm achieves favorable segmentation performance against the state-of-the-art methods in terms of visual quality and accuracy.
|Title of host publication
|Proceedings - 2013 IEEE International Conference on Computer Vision, ICCV 2013
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2013
|2013 14th IEEE International Conference on Computer Vision, ICCV 2013 - Sydney, NSW, Australia
Duration: 2013 Dec 1 → 2013 Dec 8
|Proceedings of the IEEE International Conference on Computer Vision
|2013 14th IEEE International Conference on Computer Vision, ICCV 2013
|13/12/1 → 13/12/8
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition