@article{1491,
  author = {Ameni YANGUI JAMMOUSSI, Sameh FAKHFAKH GHRIBI, Dorra SELLAMI MASMOUDI},
  title = {Adaboost Based Object Detector Optimization With Genetic Algorithm},
  journal = {Journal of Information Technology Review},
  year = {2014},
  volume = {5},
  number = {1},
  doi = {},
  url = {http://www.dline.info/jitr/fulltext/v5n1/2.pdf},
  abstract = {Object detection has gained a great deal of attention due to the requirements in many real world applications. Adaboost based object detector have shown good results in terms of accuracy and speed. It was not until this method that object detection became widely used in real world applications. The proposed Adaboost based detectors works well, though challenges and difficulties still remain, most of which are mainly related to a large number of examples of the training sets and long training process. A large number of features are required to be selected for Adaboost which slows down training process. In this work, we make headway toward reducing the number of features. The Genetic Adaboost method is proposed to select the most relevant features and discard redundant features. The number of features for a cascade structure was reduced to 57% compared to a standard Adaboost face detector.},
}