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» E-Learning Applied To Computer Vision
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100
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ICGA
1997
133views Optimization» more  ICGA 1997»
15 years 4 months ago
Messy Genetic Algorithms for Subset Feature Selection
Subset Feature Selection problems can have severalattributes which may make Messy Genetic Algorithms an appropriateoptimization method. First, competitive solutions may often use ...
L. Darrell Whitley, J. Ross Beveridge, Cesar Guerr...
185
Voted
CVPR
2012
IEEE
13 years 5 months ago
Leveraging category-level labels for instance-level image retrieval
In this article, we focus on the problem of large-scale instance-level image retrieval. For efficiency reasons, it is common to represent an image by a fixed-length descriptor w...
Albert Gordo, José A. Rodríguez-Serr...
148
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Publication
922views
16 years 9 months ago
Multi-Class Active Learning for Image Classification
One of the principal bottlenecks in applying learning techniques to classification problems is the large amount of labeled training data required. Especially for images and video, ...
Ajay J. Joshi, Fatih Porikli, Nikolaos Papanikolop...
157
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ICCV
2009
IEEE
1425views Computer Vision» more  ICCV 2009»
16 years 5 months ago
Fast Ray Features for Learning Irregular Shapes
We introduce a new class of image features, the Ray feature set, that consider image characteristics at distant contour points, capturing information which is difficult to repre...
Kevin Smith, Alan Carleton, Vincent Lepetit
CVPR
2005
IEEE
16 years 4 months ago
Geo-Consistency for Wide Multi-Camera Stereo
This paper presents a new model to overcome the occlusion problems coming from wide baseline multiple camera stereo. Rather than explicitly modeling occlusions in the matching cos...
Marc-Antoine Drouin, Martin Trudeau, Sébast...