Discriminative Learning of Visual Words for 3D Human Pose Estimation Huazhong Ning1 Wei Xu2 Yihong Gong2 Thomas Huang1 1 Dept. of ECE, U. of Illinois at Urbana-Champaign, USA. {hning2,huang}@ifp.uiuc.edu. 2 NEC Laborator

Source URL: www.ifp.illinois.edu

• Computer vision
• Artificial intelligence
• Vision
• Robot control
• Conference on Computer Vision and Pattern Recognition
• Bag-of-words model in computer vision
• Outline of object recognition
• Pose
• Visual Word
• Visual descriptor
• Feature detection
• KadirBrady saliency detector

1 Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models Zhuowen Tu, Katherine L. Narr, Piotr Doll´ar, Ivo Dinov, Paul M. Thompson, and Arthur W. Toga

Source URL: pages.ucsd.edu

• Artificial intelligence
• Machine learning
• Learning
• Graphical models
• Image segmentation
• Discriminative model
• Principal component analysis
• Conditional random field
• AdaBoost
• Functional magnetic resonance imaging
• Markov random field
• Bag-of-words model in computer vision

STRUCTURED DISCRIMINATIVE MODELS USING DEEP NEURAL-NETWORK FEATURES R. C. van Dalen, J. Yang, H. Wang, A. Ragni, C. Zhang, M. J. F. Gales Department of Engineering, University of Cambridge, United Kingdom ABSTRACT State-

Source URL: mi.eng.cam.ac.uk

• Markov models
• Machine learning
• Artificial intelligence
• Learning
• Computational linguistics
• Speech recognition software
• Computer accessibility
• Speech recognition
• Hidden Markov model
• Conditional random field
• Language model
• Image segmentation

Structured and Infinite Discriminative Models for Speech Recognition Jingzhou Yang Homerton College

Source URL: mi.eng.cam.ac.uk

• Statistics
• Learning
• Artificial intelligence
• Machine learning
• Statistical models
• Statistical inference
• Graphical models
• Structured prediction
• Discriminative model
• Conditional random field
• Generative model
• Hidden Markov model

DETECTING UNSAFE DRIVING PATTERNS USING DISCRIMINATIVE LEARNING Yue Zhou†, Wei Xu‡, Huazhong Ning†, Yihong Gong‡, Thomas S. Huang† †Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL 61

Source URL: www.ifp.illinois.edu

• Machine learning
• Conditional random field
• Pattern recognition
• Supervised learning
• Hidden Markov model
• Support vector machine
• Semi-supervised learning
• Expectationmaximization algorithm
• Mixture model
• Artificial neural network
• Viterbi algorithm

Learning Generative Models via Discriminative Approaches Zhuowen Tu Lab of Neuro Imaging, UCLA Abstract

Source URL: pages.ucsd.edu

• Machine learning
• Statistical models
• Discriminative model
• Generative model
• Independent component analysis
• Image segmentation
• Gibbs sampling
• Linear classifier

A Discriminative Hierarchical Model for Fast Coreference at Large Scale Michael Wick University of Massachsetts 140 Governor’s Drive Amherst, MA

Source URL: people.cs.umass.edu

Loose Shape Model for Discriminative Learning of Object Categories

Source URL: rita.osadchy.net

Which is More Suitable for Chinese Word Segmentation, the Generative Model or the Discriminative One? ∗ F a

Source URL: aclweb.org

• Machine learning
• Artificial intelligence
• Learning
• Computational linguistics
• Language modeling
• Natural language processing
• Statistical natural language processing
• N-gram
• Bigram
• Generative model
• Language model
• Pattern recognition

A Comparison of Algorithms for Inference and Learning in Probabilistic Graphical Models Brendan J. Frey and Nebojsa Jojic Abstract Computer vision is currently one of the most exciting areas of artificial intelligence re

Source URL: www.isit2004.org

• Statistics
• Statistical models
• Probability
• Graphical models
• Machine learning
• Statistical theory
• Bayesian statistics
• Markov random field
• Bayesian network
• Generative model
• Factor graph
• Discriminative model