Random convolution ensembles

Authors:
Michael Mayo
Affiliations:
Dept. of Computer Science, University of Waikato, Hamilton, New Zealand
Venue:
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Year:
2007

Citing 10
Cited 0

Original Contribution: Stacked generalization

Neural Networks
Bagging predictors

Machine Learning
Comparing images using color coherence vectors

MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Locally Weighted Learning

Artificial Intelligence Review - Special issue on lazy learning
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
Practical algorithms for image analysis: description, examples, and code

Practical algorithms for image analysis: description, examples, and code
Random Forests

Machine Learning
Improvements to Platt's SMO Algorithm for SVM Classifier Design

Neural Computation
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
An Experimental Study on Pedestrian Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.