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» A Framework for Multiple-Instance Learning
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CVPR
2009
IEEE
16 years 11 months ago
Learning Optimized MAP Estimates in Continuously-Valued MRF Models
We present a new approach for the discriminative training of continuous-valued Markov Random Field (MRF) model parameters. In our approach we train the MRF model by optimizing t...
Kegan G. G. Samuel, Marshall F. Tappen
CVPR
2006
IEEE
16 years 6 months ago
Correlated Label Propagation with Application to Multi-label Learning
Many computer vision applications, such as scene analysis and medical image interpretation, are ill-suited for traditional classification where each image can only be associated w...
Feng Kang, Rong Jin, Rahul Sukthankar
CVPR
2008
IEEE
16 years 6 months ago
Learning for stereo vision using the structured support vector machine
We present a random field based model for stereo vision with explicit occlusion labeling in a probabilistic framework. The model employs non-parametric cost functions that can be ...
Yunpeng Li, Daniel P. Huttenlocher
CVPR
2008
IEEE
16 years 6 months ago
Kernel-based learning of cast shadows from a physical model of light sources and surfaces for low-level segmentation
In background subtraction, cast shadows induce silhouette distortions and object fusions hindering performance of high level algorithms in scene monitoring. We introduce a nonpara...
André Zaccarin, Nicolas Martel-Brisson
ECCV
2006
Springer
16 years 6 months ago
Learning to Combine Bottom-Up and Top-Down Segmentation
Bottom-up segmentation based only on low-level cues is a notoriously difficult problem. This difficulty has lead to recent top-down segmentation algorithms that are based on class-...
Anat Levin, Yair Weiss