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PCM
2007
Springer

Random Convolution Ensembles

13 years 9 months ago
Random Convolution Ensembles
A novel method for creating diverse ensembles of image classifiers is proposed. The idea is that, for each base image classifier in the ensemble, a random image transformation is generated and applied to all of the images in the labeled training set. The base classifiers are then learned using features extracted from these randomly transformed versions of the training data, and the result is a highly diverse ensemble of image classifiers. This approach is evaluated on a benchmark pedestrian detection dataset and shown to be effective.
Michael Mayo
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where PCM
Authors Michael Mayo
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