Abstract
This paper describes a new approach using particle swarm optimisation (PSO) within AdaBoost for object detection. Instead of using the time consuming exhaustive search for finding good features to be used for constructing weak classifiers in AdaBoost, we propose two PSO based methods in this paper. The first uses PSO to evolve and select the good features only and the weak classifiers use a kind of decision stump. The second uses PSO for both selecting the good features and evolving weak classifiers in parallel. These two methods are examined and compared on a pasta detection data set. The experiment results show that both approaches perform quite well for the pasta detection problem, and that using PSO for selecting good individual features and evolving associated weak classifiers in AdaBoost is more effective than for selecting features only for this problem.
