%0 Journal Article %A Doaa Hegazy and Joachim Denzler %D 2008 %J International Journal of Computer and Information Engineering %B World Academy of Science, Engineering and Technology %I Open Science Index 21, 2008 %T Performance Comparison and Evaluation of AdaBoost and SoftBoost Algorithms on Generic Object Recognition %U https://publications.waset.org/pdf/5048 %V 21 %X SoftBoost is a recently presented boosting algorithm, which trades off the size of achieved classification margin and generalization performance. This paper presents a performance evaluation of SoftBoost algorithm on the generic object recognition problem. An appearance-based generic object recognition model is used. The evaluation experiments are performed using a difficult object recognition benchmark. An assessment with respect to different degrees of label noise as well as a comparison to the well known AdaBoost algorithm is performed. The obtained results reveal that SoftBoost is encouraged to be used in cases when the training data is known to have a high degree of noise. Otherwise, using Adaboost can achieve better performance. %P 2893 - 2897