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ICML
2010
IEEE
15 years 7 months ago
Bottom-Up Learning of Markov Network Structure
The structure of a Markov network is typically learned using top-down search. At each step, the search specializes a feature by conjoining it to the variable or feature that most ...
Jesse Davis, Pedro Domingos
ICCV
2009
IEEE
1198views Computer Vision» more  ICCV 2009»
16 years 11 months ago
Implicit Color Segmentation Features for Pedestrian Detection
We investigate the problem of pedestrian detection in still images. Sliding window classifiers, notably using the Histogram-of-Gradient (HOG) features proposed by Dalal and Trig...
Patrick Ott and Mark Everingham
EJIVP
2010
107views more  EJIVP 2010»
15 years 1 months ago
Unsupervised Action Classification Using Space-Time Link Analysis
Abstract--In this paper we address the problem of unsupervised discovery of action classes in video data. Different from all existing methods thus far proposed for this task, we pr...
Haowei Liu, Rogerio Feris, Volker Krüger, Min...
ICIP
2001
IEEE
16 years 8 months ago
Higher order autocorrelations for pattern classification
The use of higher-order local autocorrelations as features for pattern recognition has been acknowledged since many years, but their applicability was restricted to relatively low...
Vlad Popovici, Jean-Philippe Thiran
CVPR
2006
IEEE
16 years 8 months ago
Accurate Face Alignment using Shape Constrained Markov Network
In this paper, we present a shape constrained Markov network for accurate face alignment. The global face shape is defined as a set of weighted shape samples which are integrated ...
Lin Liang, Fang Wen, Ying-Qing Xu, Xiaoou Tang, He...