Fast Weak Learner Based on Genetic Algorithm

An approach to the acceleration of parametric weak classifier boosting is proposed. Weak classifier is called parametric if it has fixed number of parameters and, so, can be represented as a point into multidimensional space. Genetic algorithm is used instead of exhaustive search to learn parameters of such classifier. Proposed approach also takes cases when effective algorithm for learning some of the classifier parameters exists into account. Experiments confirm that such an approach can dramatically decrease classifier training time while keeping both training and test errors small.Comment: 4 pages, acmsiggraph latex style packed with the latex source in the single archiv

GA Based Feature Generation for Training Cascade Object Detector

Viola et al. have introduced a fast object detection scheme based on a boosted cascade of haar-like features. In this paper, we introduce a novel ternary feature that enriches the diversity and the flexibility significantly over haar-like features. We also introduce a new genetic algorithm based method for training effective ternary features. Experimental results showed that the rejection rate can reach at 98.5 % with only 16 features at the first layer of the cascade detector. We confirmed that the training time can be significantly shortened while the performance of the resulted cascade detector is comparable to the previous methods. 1