iccv iccv2013 iccv2013-150 iccv2013-150-reference knowledge-graph by maker-knowledge-mining
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Author: Jimei Yang, Yi-Hsuan Tsai, Ming-Hsuan Yang
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 achievesfavorable segmentationperformance against the state-of-the-art methods in terms of visual quality and accuracy.