Labeling of Textured Data with Co-training and Active Learning

In this paper, we present a robust texture labeling method that requires minimum user interaction. Initially only a fraction of the textures needs to be manually labeled, and then a co-training procedure is used to automatically label most of the unlabeled samples. Simultanously an active learning framework is used to learn those unlabeled samples that would provide much information for the system if labeled. Samples found by active learning are labeled explicitly with a visualization-based approach which provides a very user-friendly view into the data and enables possibility to learn new classes. In the experiments, the labeling framework is applied to real texture image data for building a training set for a classifier.