TY - GEN
T1 - Multicue graph mincut for image segmentation
AU - Feng, Wei
AU - Xie, Lei
AU - Liu, Zhi Qiang
PY - 2010
Y1 - 2010
N2 - We propose a general framework to encode various grouping cues for natural image segmentation. We extend the classical Gibbs energy of an MRF to three terms: likelihood energy, coherence energy and separating energy. We encode generative cues in the likelihood and coherence energy to ensure the goodness and feasibility of segmentation, and embed discriminative cues in the separating energy to encourage assigning two pixels with strong separability with different labels. We use a self-validated process to iteratively minimize the global Gibbs energy. Our approach is able to automatically determine the number of segments, and produce a natural hierarchy of coarse-to-fine segmentation. Experiments show that our approach works well for various segmentation problems, and outperforms existing methods in terms of robustness to noise and preservation of soft edges.
AB - We propose a general framework to encode various grouping cues for natural image segmentation. We extend the classical Gibbs energy of an MRF to three terms: likelihood energy, coherence energy and separating energy. We encode generative cues in the likelihood and coherence energy to ensure the goodness and feasibility of segmentation, and embed discriminative cues in the separating energy to encourage assigning two pixels with strong separability with different labels. We use a self-validated process to iteratively minimize the global Gibbs energy. Our approach is able to automatically determine the number of segments, and produce a natural hierarchy of coarse-to-fine segmentation. Experiments show that our approach works well for various segmentation problems, and outperforms existing methods in terms of robustness to noise and preservation of soft edges.
UR - http://www.scopus.com/inward/record.url?scp=78650463100&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-12304-7_67
DO - 10.1007/978-3-642-12304-7_67
M3 - 会议稿件
AN - SCOPUS:78650463100
SN - 3642123031
SN - 9783642123030
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 707
EP - 717
BT - Computer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers
T2 - 9th Asian Conference on Computer Vision, ACCV 2009
Y2 - 23 September 2009 through 27 September 2009
ER -