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» MuFeSaC: Learning When to Use Which Feature Detector
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WWW
2008
ACM
14 years 9 months ago
Detecting image spam using visual features and near duplicate detection
Email spam is a much studied topic, but even though current email spam detecting software has been gaining a competitive edge against text based email spam, new advances in spam g...
Bhaskar Mehta, Saurabh Nangia, Manish Gupta 0002, ...
ICMI
2007
Springer
112views Biometrics» more  ICMI 2007»
14 years 3 months ago
How to distinguish posed from spontaneous smiles using geometric features
Automatic distinction between posed and spontaneous expressions is an unsolved problem. Previously cognitive sciences’ studies indicated that the automatic separation of posed f...
Michel François Valstar, Hatice Gunes, Maja...
CVPR
2010
IEEE
14 years 2 months ago
Facial Point Detection using Boosted Regression and Graph Models
Finding fiducial facial points in any frame of a video showing rich naturalistic facial behaviour is an unsolved problem. Yet this is a crucial step for geometric-featurebased fa...
Michel Valstar, Brais Martinez, Xavier Binefa, Maj...
ICML
2006
IEEE
14 years 10 months ago
A choice model with infinitely many latent features
Elimination by aspects (EBA) is a probabilistic choice model describing how humans decide between several options. The options from which the choice is made are characterized by b...
Carl Edward Rasmussen, Dilan Görür, Fran...
ICML
2007
IEEE
14 years 10 months ago
Feature selection in a kernel space
We address the problem of feature selection in a kernel space to select the most discriminative and informative features for classification and data analysis. This is a difficult ...
Bin Cao, Dou Shen, Jian-Tao Sun, Qiang Yang, Zheng...