GAdaBoost：用遗传算法加速Adaboost特征选择
GAdaBoost: Accelerating Adaboost Feature Selection with Genetic Algorithms
摘要：
由Viola和Jones提出的Boosted cascade of simple features是最著名的目标检测框架之一。然而，它的培训过程很长。这是由于Adaboost庞大的特征空间和详尽的搜索性质。在本文中，我们提出了GAdaboost：一种遗传算法，通过自然的特征选择来加速训练过程。具体来说，我们建议限制Adaboost搜索的一个子集内的巨大的特征空间，而这个子集的发展遵循遗传算法。实验表明，我们提出的GAdaboost比Adaboost快3.7倍。我们还证明了这种加速的代价是在FDDB基准人脸检测集和Caltech Web Faces上分别测试时检测精度仅下降（3%，4%）。
英文摘要：
Boosted cascade of simple features, by Viola and Jones, is one of the most famous object detection frameworks. However, it suffers from a lengthy training process. This is due to the vast features space and the exhaustive search nature of Adaboost. In this paper we propose GAdaboost: a Genetic Algorithm to accelerate the training procedure through natural feature selection. Specifically, we propose to limit Adaboost search within a subset of the huge feature space, while evolving this subset following a Genetic Algorithm. Experiments demonstrate that our proposed GAdaboost is up to 3.7 times faster than Adaboost. We also demonstrate that the price of this speedup is a mere decrease (3%, 4%) in detection accuracy when tested on FDDB benchmark face detection set, and Caltech Web Faces respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
