Combining Adaboost learning and evolutionary search to select features for real-time object detection
- 17 January 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 2107-2113 Vol.2
- https://doi.org/10.1109/cec.2004.1331156
Abstract
Recently, P. Viola and M.J. Jones (2001) presented a method for real-time object detection in images using a boosted cascade of simple features. In This work we show how an evolutionary algorithm can be used within the Adaboost framework to find new features providing better classifiers. The evolutionary algorithm replaces the exhaustive search over all features so that even very large feature sets can be searched in reasonable time. Experiments on two different sets of images prove that by the use of evolutionary search we are able to find object detectors that are faster and have higher detection rates.Keywords
This publication has 10 references indexed in Scilit:
- Visual Learning by Evolutionary and Coevolutionary Feature SynthesisIEEE Transactions on Evolutionary Computation, 2007
- Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction.Published by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object DetectionPublished by Springer Nature ,2003
- Evolving visual features and detectorsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A statistical method for 3D object detection applied to faces and carsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)The Annals of Statistics, 2000
- Evolution of Ship Detectors for Satellite SAR ImageryPublished by Springer Nature ,1999
- Neural network-based face detectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1998
- Example-based learning for view-based human face detectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1998
- Using Learning to Facilitate the Evolution of Features for Recognizing Visual ConceptsEvolutionary Computation, 1996