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» MuFeSaC: Learning When to Use Which Feature Detector
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CVPR
2006
IEEE
14 years 10 months ago
Incremental learning of object detectors using a visual shape alphabet
We address the problem of multiclass object detection. Our aims are to enable models for new categories to benefit from the detectors built previously for other categories, and fo...
Andreas Opelt, Axel Pinz, Andrew Zisserman
CLOR
2006
13 years 11 months ago
Object Detection and Localization Using Local and Global Features
Traditional approaches to object detection only look at local pieces of the image, whether it be within a sliding window or the regions around an interest point detector. However, ...
Kevin P. Murphy, Antonio B. Torralba, Daniel Eaton...
PAMI
2002
112views more  PAMI 2002»
13 years 7 months ago
Recognizing Handwritten Digits Using Hierarchical Products of Experts
The product of experts learning procedure [1] can discover a set of stochastic binary features that constitute a nonlinear generative model of handwritten images of digits. The qua...
Guy Mayraz, Geoffrey E. Hinton
DSP
2006
13 years 7 months ago
Detection of audio covert channels using statistical footprints of hidden messages
We address the problem of detecting the presence of hidden messages in audio. The detector is based on the characteristics of the denoised residuals of the audio file, which may c...
Hamza Özer, Bülent Sankur, Nasir D. Memo...
TRECVID
2008
13 years 9 months ago
Learning TRECVID'08 High-Level Features from YouTube
Run No. Run ID Run Description infMAP (%) training on TV08 data 1 IUPR-TV-M SIFT visual words with maximum entropy 6.1 2 IUPR-TV-MF SIFT with maximum entropy, fused with color+tex...
Adrian Ulges, Christian Schulze, Markus Koch, Thom...