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» Learning Flexible Features for Conditional Random Fields
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CVPR
2007
IEEE
15 years 11 months ago
Learning to Detect A Salient Object
We study visual attention by detecting a salient object in an input image. We formulate salient object detection as an image segmentation problem, where we separate the salient obj...
Tie Liu, Jian Sun, Nanning Zheng, Xiaoou Tang, Heu...
INLG
2010
Springer
14 years 7 months ago
Poly-co: An Unsupervised Co-reference Detection System
We describe our contribution to the Generation Challenge 2010 for the tasks of Named Entity Recognition and coreference detection (GREC-NER). To extract the NE and the referring e...
Eric Charton, Michel Gagnon, Benoît Ozell
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ICPR
2008
IEEE
15 years 11 months ago
Image objects and multi-scale features for annotation detection
This paper investigates several issues in the problem of detecting handwritten markings, or annotations, on printed documents. One issue is to define the appropriate units over wh...
Eric Saund, Jindong Chen, Yizhou Wang
ICML
2003
IEEE
15 years 10 months ago
Hidden Markov Support Vector Machines
This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines an...
Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofm...
ICDAR
2011
IEEE
13 years 9 months ago
On-line Handwritten Japanese Characters Recognition Using a MRF Model with Parameter Optimization by CRF
— This paper describes a Markov random field (MRF) model with weighting parameters optimized by conditional random field (CRF) for on-line recognition of handwritten Japanese cha...
Bilan Zhu, Masaki Nakagawa