Soft label based semi-supervised boosting for classification and object recognition
Dingfu Zhou, Benjamin Quost, Vincent Frémont · 2014
Supervised classification algorithms such as Boosting and SVM have achieved significant success in the field of computer vision for classification and object recognition. However, the performance of the classifier decreases rapidly if there are insufficient labeled training samples. In this paper, a semi-supervised boosting algorithm is proposed to overcome this limitation. First, a few labeled instances are use to estimate probabilistic class labels for unlabeled samples using Gaussian Mixture Models after a dimension reduction step performed via Principal Component Analysis. Then, we apply a boosting strategy on decision stumps trained using the soft labeled instances thus obtained. The performances of our strategy are evaluated on several state-of-the-art classification datasets, as well as on a pedestrian detection and recognition problem. Experimental results demonstrate the interest of taking into account additional data in the training process.