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DAGM
2004
Springer
15 years 2 months ago
Learning from Labeled and Unlabeled Data Using Random Walks
We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to...
Dengyong Zhou, Bernhard Schölkopf
CORR
2012
Springer
272views Education» more  CORR 2012»
13 years 5 months ago
Fast and Exact Top-k Search for Random Walk with Restart
Graphs are fundamental data structures and have been employed for centuries to model real-world systems and phenomena. Random walk with restart (RWR) provides a good proximity sco...
Yasuhiro Fujiwara, Makoto Nakatsuji, Makoto Onizuk...
SIBGRAPI
2006
IEEE
15 years 3 months ago
Improving 2D mesh image segmentation with Markovian Random Fields
Traditional mesh segmentation methods normally operate on geometrical models with no image information. On the other hand, 2D image-based mesh generation and segmentation counterp...
Alex Jesus Cuadros-Vargas, Leandro C. Gerhardinger...
ICIP
2010
IEEE
14 years 7 months ago
Fast semantic scene segmentation with conditional random field
In this paper, we present a fast approach to obtain semantic scene segmentation with high precision. We employ a two-stage classifier to label all image pixels. First, we use the ...
Wen Yang, Dengxin Dai, Bill Triggs, Gui-Song Xia, ...
ICML
2009
IEEE
15 years 10 months ago
Multi-class image segmentation using conditional random fields and global classification
A key aspect of semantic image segmentation is to integrate local and global features for the prediction of local segment labels. We present an approach to multi-class segmentatio...
Nils Plath, Marc Toussaint, Shinichi Nakajima