Exemplar Cut

Jimei Yang, Yi-Hsuan Tsai, Ming-Hsuan Yang; The IEEE International Conference on Computer Vision (ICCV), 2013, pp. 857-864

Abstract


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.

Related Material


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[bibtex]
@InProceedings{Yang_2013_ICCV,
author = {Yang, Jimei and Tsai, Yi-Hsuan and Yang, Ming-Hsuan},
title = {Exemplar Cut},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}
}