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» MuFeSaC: Learning When to Use Which Feature Detector
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ECCV
2010
Springer
14 years 2 months ago
Detecting ground shadows in outdoor consumer photographs
Detecting shadows from images can significantly improve the performance of several vision tasks such as object detection and tracking. Recent approaches have mainly used illuminat...
JMLR
2011
192views more  JMLR 2011»
13 years 4 months ago
Minimum Description Length Penalization for Group and Multi-Task Sparse Learning
We propose a framework MIC (Multiple Inclusion Criterion) for learning sparse models based on the information theoretic Minimum Description Length (MDL) principle. MIC provides an...
Paramveer S. Dhillon, Dean P. Foster, Lyle H. Unga...
PRL
2007
138views more  PRL 2007»
13 years 8 months ago
Ent-Boost: Boosting using entropy measures for robust object detection
Recently, boosting has come to be used widely in object-detection applications because of its impressive performance in both speed and accuracy. However, learning weak classifier...
Duy-Dinh Le, Shin'ichi Satoh
CVPR
2010
IEEE
14 years 5 months ago
Robust Classification of Objects, Faces, and Flowers Using Natural Image Statistics
Classification of images in many category datasets has rapidly improved in recent years. However, systems that perform well on particular datasets typically have one or more lim...
Christopher Kanan, Garrison Cottrell
IJCAI
1993
13 years 10 months ago
The Statistical Learning of Accurate Heuristics
Heuristics used by search algorithms are usually composed of more primitive functions which we call "features". A method for combining features is presented which is bas...
Anna Bramanti-Gregor, Henry W. Davis