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» Hidden Conditional Random Fields for Meeting Segmentation
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AAAI
2008
15 years 2 months ago
Hidden Dynamic Probabilistic Models for Labeling Sequence Data
We propose a new discriminative framework, namely Hidden Dynamic Conditional Random Fields (HDCRFs), for building probabilistic models which can capture both internal and external...
Xiaofeng Yu, Wai Lam
PREMI
2009
Springer
15 years 7 months ago
Unsupervised Color Image Segmentation Using Compound Markov Random Field Model
Abstract. In this paper, we propose an unsupervised color image segmentation scheme using homotopy continuation method and Compound Markov Random Field (CMRF) model. The proposed s...
Sucheta Panda, P. K. Nanda
78
Voted
ICMCS
2007
IEEE
151views Multimedia» more  ICMCS 2007»
15 years 6 months ago
Exploring Contextual Information in a Layered Framework for Group Action Recognition
Contextual information is important for sequence modeling. Hidden Markov Models (HMMs) and extensions, which have been widely used for sequence modeling, make simplifying, often u...
Dong Zhang, Samy Bengio
PAMI
2007
123views more  PAMI 2007»
14 years 12 months ago
Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields
—Recent developments in statistical theory and associated computational techniques have opened new avenues for image modeling as well as for image segmentation techniques. Thus, ...
Dalila Benboudjema, Wojciech Pieczynski
PAMI
2011
14 years 3 months ago
Hidden Part Models for Human Action Recognition: Probabilistic versus Max Margin
—We present a discriminative part-based approach for human action recognition from video sequences using motion features. Our model is based on the recently proposed hidden condi...
Yang Wang 0003, Greg Mori