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» Learning a Classification Model for Segmentation
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ICCV
2009
IEEE
15 years 2 months ago
Level Set Segmentation with Both Shape and Intensity Priors
We present a new variational level-set-based segmentation formulation that uses both shape and intensity prior information learned from a training set. By applying Bayes’ rule...
Siqi Chen and Richard J. Radke
FGR
2008
IEEE
346views Biometrics» more  FGR 2008»
14 years 3 months ago
Markov random field models for hair and face segmentation
This paper presents an algorithm for measuring hair and face appearance in 2D images. Our approach starts by using learned mixture models of color and location information to sugg...
Kuang-chih Lee, Dragomir Anguelov, Baris Sumengen,...
ICDM
2010
IEEE
197views Data Mining» more  ICDM 2010»
13 years 7 months ago
D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-defined Classification
: D-LDA: A Topic Modeling Approach without Constraint Generation for Semi-Defined Classification Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong, Zhongzhi Shi HP Labo...
Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yu...
EMNLP
2006
13 years 10 months ago
Learning Information Status of Discourse Entities
In this paper we address the issue of automatically assigning information status to discourse entities. Using an annotated corpus of conversational English and exploiting morpho-s...
Malvina Nissim
ACL
2009
13 years 7 months ago
Learning with Annotation Noise
It is usually assumed that the kind of noise existing in annotated data is random classification noise. Yet there is evidence that differences between annotators are not always ra...
Eyal Beigman, Beata Beigman Klebanov