Author :
Jimei Yang ; Yi-Hsuan Tsai ; Ming-Hsuan Yang
Author_Institution :
Univ. of California, Merced, Merced, CA, USA
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.
Keywords :
graph theory; image segmentation; Graz datasets; PASCAL datasets; class-specific object segmentation hypotheses; energy function; exemplar augmented graph cuts; hierarchical region tree; hybrid parametric and nonparametric algorithm; pylon model; Computational modeling; Image segmentation; Labeling; Object segmentation; Poles and towers; Training; Vectors;
Conference_Titel :
Computer Vision (ICCV), 2013 IEEE International Conference on
Conference_Location :
Sydney, VIC
DOI :
10.1109/ICCV.2013.111