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» Multiscale Conditional Random Fields for Image Labeling
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CVPR
2008
IEEE
16 years 4 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
PAMI
2008
198views more  PAMI 2008»
15 years 1 months ago
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors
Among the most exciting advances in early vision has been the development of efficient energy minimization algorithms for pixel-labeling tasks such as depth or texture computation....
Richard Szeliski, Ramin Zabih, Daniel Scharstein, ...
128
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AI
2011
Springer
14 years 5 months ago
Exploiting Conversational Features to Detect High-Quality Blog Comments
Abstract. In this work, we present a method for classifying the quality of blog comments using Linear-Chain Conditional Random Fields (CRFs). This approach is found to yield high a...
Nicholas FitzGerald, Giuseppe Carenini, Gabriel Mu...
ICIP
2001
IEEE
16 years 3 months ago
Coding theoretic approach to image segmentation
This paper introduces multi-scale tree-based approaches to image segmentation, using Rissanen's coding theoretic minimum description length (MDL) principle to penalize overly...
Mário A. T. Figueiredo, Robert D. Nowak, Un...
CVPR
2010
IEEE
15 years 7 months ago
YouTubeCat: Learning to Categorize Wild Web Videos
Automatic categorization of videos in a Web-scale unconstrained collection such as YouTube is a challenging task. A key issue is how to build an effective training set in the pres...
Zheshen Wang, Ming Zhao, Yang Song, Sanjiv Kumar, ...