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» Boosting Object Detection Using Feature Selection
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CVPR
2008
IEEE
16 years 3 months ago
Unsupervised feature selection via distributed coding for multi-view object recognition
Object recognition accuracy can be improved when information from multiple views is integrated, but information in each view can often be highly redundant. We consider the problem...
Chris Mario Christoudias, Raquel Urtasun, Trevor D...
ASIAN
2004
Springer
117views Algorithms» more  ASIAN 2004»
15 years 7 months ago
The Feature Selection and Intrusion Detection Problems
Cyber security is a serious global concern. The potential of cyber terrorism has posed a threat to national security; meanwhile the increasing prevalence of malware and incidents o...
Andrew H. Sung, Srinivas Mukkamala
ICASSP
2011
IEEE
14 years 5 months ago
Generic object recognition using automatic region extraction and dimensional feature integration utilizing multiple kernel learn
Recently, in generic object recognition research, a classification technique based on integration of image features is garnering much attention. However, with a classifying techn...
Toru Nakashika, Akira Suga, Tetsuya Takiguchi, Yas...
CVPR
2005
IEEE
16 years 3 months ago
WaldBoost - Learning for Time Constrained Sequential Detection
: In many computer vision classification problems, both the error and time characterizes the quality of a decision. We show that such problems can be formalized in the framework of...
Jan Sochman, Jiri Matas
FGR
2004
IEEE
231views Biometrics» more  FGR 2004»
15 years 5 months ago
A Boosted Classifier Tree for Hand Shape Detection
The ability to detect a persons unconstrained hand in a natural video sequence has applications in sign language, gesture recognition and HCI. This paper presents a novel, unsuper...
Eng-Jon Ong, Richard Bowden