Online AdaBoost ECOC for image classification
Hongwen Huo, Jufu Feng · 2010
We present a novel online algorithm called online AdaBoost ECOC (error-correcting output codes) for image classification problems. In recent years, AdaBoost is very successful in many domains such as object detection in images and videos. It is a representative large margin classifier for binary classification problems and is efficient for on-line learning. However, image classification is a typical multi-class problem. It is difficult to use AdaBoost here, especially in an online version of image classification problem. In this paper, we combine online AdaBoost and ECOC algorithm to solve online multi-class image classification problems. We perform online AdaBoost ECOC on MNIST handwritten digit, ORL face and UCI image database. The results show our algorithm's accuracy and robustness.