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» Learning Mid-Level Features For Recognition
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CVPR
2010
IEEE
14 years 1 months ago
Learning Mid-Level Features For Recognition
Many successful models for scene or object recognition transform low-level descriptors (such as Gabor filter responses, or SIFT descriptors) into richer representations of interme...
Y-Lan Boureau, Francis Bach, Yann LeCun, Jean Ponc...
MMM
2007
Springer
176views Multimedia» more  MMM 2007»
13 years 11 months ago
An Efficient Automatic Video Shot Size Annotation Scheme
Abstract. This paper presents an efficient learning scheme for automatic annotation of video shot size. Instead of existing methods that applied in sports videos using domain knowl...
Meng Wang, Xian-Sheng Hua, Yan Song, Wei Lai, Li-R...
FGR
2011
IEEE
255views Biometrics» more  FGR 2011»
12 years 8 months ago
Beyond simple features: A large-scale feature search approach to unconstrained face recognition
— Many modern computer vision algorithms are built atop of a set of low-level feature operators (such as SIFT [1], [2]; HOG [3], [4]; or LBP [5], [6]) that transform raw pixel va...
David D. Cox, Nicolas Pinto
ICASSP
2011
IEEE
12 years 8 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...
PR
2007
148views more  PR 2007»
13 years 4 months ago
Learning the best subset of local features for face recognition
We propose a novel, local feature-based face representation method based on twostage subset selection where the first stage finds the informative regions and the second stage ...
Berk Gökberk, M. Okan Irfanoglu, Lale Akarun,...