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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...
This paper describes an application of a particle swarm optimisation based AdaBoost algorithm to classify human facial expressions. The particle swarm is used to choose optimal Haar features for constructing weak classifiers within AdaBoost. This algorithm is trained using the Japanese Female Facial Expression dataset and tested on the Cohn-Kanade AU-Coded Face Expression Database. The results show...
The high number of features in many machine vision applications has a major impact on the performance of machine learning algorithms. Feature selection (FS) is an avenue to dimensionality reduction. Evolutionary search techniques have been very promising in finding solutions in the exponentially growing search space of FS problems. This paper proposes a genetic programming (GP) approach to FS where...
This paper proposes a PSOAdaBoost algorithm incorporating particle swarm optimization within an AdaBoost framework for face detection applications. The basic component of an AdaBoost detector is a weak classifier, consisting of a feature, selected by an exhaustive search mechanism, and a decision threshold. The proposed PSOAdaBoost computes the best feature and optimizes the threshold in one optimization...
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