Combining Classifiers for Improved Multilabel Image Classification
We propose a stacking-like method for multilabel image classification. Our approach combines the output of binary base learners, which use different features for image description, in a simple and straightforward way: The confidence values of the base learners are fed into a support vector machine (SVM) in order to improve prediction accuracy. Experiments on the datasets of the Pascal Visual Object Classes challenges (VOC) of 2006 and~2007 show that our method significantly improves over the performance of the base learners. Our approach also works better than more naive approaches for combining features or classifiers.