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» Learning Flexible Features for Conditional Random Fields
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CVPR
2008
IEEE
15 years 11 months ago
Auto-context and its application to high-level vision tasks
The notion of using context information for solving highlevel vision problems has been increasingly realized in the field. However, how to learn an effective and efficient context...
Zhuowen Tu
ICPR
2010
IEEE
15 years 1 months ago
Efficient Learning to Label Images
Conditional random field methods (CRFs) have gained popularity for image labeling tasks in recent years. In this paper, we describe an alternative discriminative approach, by exte...
Ke Jia, Li Cheng, Nianjun Liu, Lei Wang
ICIP
2003
IEEE
15 years 11 months ago
Random access using isolated regions
Random access is a desirable feature in many video communication systems. Intra pictures have been conventionally used as random access points, but correct picture content can als...
Miska M. Hannuksela, Ye-Kui Wang, Moncef Gabbouj
CVPR
2010
IEEE
15 years 6 months ago
A Generative Perspective on MRFs in Low-Level Vision
Markov random fields (MRFs) are popular and generic probabilistic models of prior knowledge in low-level vision. Yet their generative properties are rarely examined, while applica...
Uwe Schmidt, Qi Gao, Stefan Roth
TIST
2011
136views more  TIST 2011»
14 years 4 months ago
Probabilistic models for concurrent chatting activity recognition
Recognition of chatting activities in social interactions is useful for constructing human social networks. However, the existence of multiple people involved in multiple dialogue...
Jane Yung-jen Hsu, Chia-chun Lian, Wan-rong Jih