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» Learning to Track with Multiple Observers
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PR
2007
102views more  PR 2007»
13 years 7 months ago
A robust incremental learning framework for accurate skin region segmentation in color images
In this paper, we propose a robust incremental learning framework for accurate skin region segmentation in real-life images. The proposed framework is able to automatically learn ...
Bin Li, Xiangyang Xue, Jianping Fan
CVPR
2012
IEEE
11 years 10 months ago
Fixed-rank representation for unsupervised visual learning
Subspace clustering and feature extraction are two of the most commonly used unsupervised learning techniques in computer vision and pattern recognition. State-of-theart technique...
Risheng Liu, Zhouchen Lin, Fernando De la Torre, Z...
JMLR
2010
172views more  JMLR 2010»
13 years 2 months ago
Modeling annotator expertise: Learning when everybody knows a bit of something
Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this p...
Yan Yan, Rómer Rosales, Glenn Fung, Mark W....
GECCO
2009
Springer
204views Optimization» more  GECCO 2009»
14 years 10 days ago
Combined structure and motion extraction from visual data using evolutionary active learning
We present a novel stereo vision modeling framework that generates approximate, yet physically-plausible representations of objects rather than creating accurate models that are c...
Krishnanand N. Kaipa, Josh C. Bongard, Andrew N. M...
CVPR
2008
IEEE
14 years 9 months ago
Learning 4D action feature models for arbitrary view action recognition
In this paper we present a novel approach using a 4D (x,y,z,t) action feature model (4D-AFM) for recognizing actions from arbitrary views. The 4D-AFM elegantly encodes shape and m...
Pingkun Yan, Saad M. Khan, Mubarak Shah