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Particle swarm optimisation based AdaBoost for object detection

Authors: Ammar Mohemmed, Mark Johnston, Mengjie Zhang

Published in: Soft Computing | Issue 9/2011

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Abstract

This paper proposes a new approach to using particle swarm optimisation (PSO) within an AdaBoost framework for object detection. Instead of using exhaustive search for finding good features to be used for constructing weak classifiers in AdaBoost, we propose two methods based on PSO. The first uses PSO to evolve and select good features only, and the weak classifiers use a simple decision stump. The second uses PSO for both selecting good features and evolving weak classifiers in parallel. These two methods are examined and compared on two challenging object detection tasks in images: detection of individual pasta pieces and detection of a face. The experimental results suggest that both approaches can successfully detect object positions and that using PSO for selecting good individual features and evolving associated weak classifiers in AdaBoost is more effective than for selecting features only. We also show that PSO can evolve and select meaningful features in the face detection task.

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Title: Particle swarm optimisation based AdaBoost for object detection
Authors: Ammar Mohemmed
Mark Johnston
Mengjie Zhang
Publication date: 01-09-2011
Publisher: Springer-Verlag
Published in: Soft Computing / Issue 9/2011
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-010-0615-x

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