中央研究院 資訊科學研究所

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.

Ming-Hsuan Yang is an associate professor in Electrical Engineering and Computer Science at University of California, Merced.

He received the PhD degree in Computer Science from the University of Illinois at Urbana-Champaign in 2000. He serves as an area chair for several conferences including IEEE Conference on Computer Vision and Pattern Recognition, IEEE International Conference on Computer Vision, European Conference on Computer Vision, Asian Conference on Computer, AAAI National Conference on Artificial Intelligence, and IEEE International Conference on Automatic Face and Gesture Recognition. He will serve as a program co-chair for Asian Conference on Computer Vision in 2014. He serves as an associate editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence (2007 to 2011), International Journal of Computer Vision, Image and Vision Computing and Journal of Artificial Intelligence Research. Yang received the Google Faculty Award in 2009, and the Distinguished Early Career Research award from the UC Merced senate in 2011, and the Faculty Early Career Development (CAREER) award from the National Science Foundation in 2012. He holds 20 US patents. He is a senior member of the IEEE and the ACM.