Batch Mode Active Learning for Semantic Segmentation Based on Multi-Clue Sample Selection

Large labeled datasets are required for training a powerful semantic segmentation model. However, it is very expensive to construct pixel-wise annotated images. In this work, we propose a general batch mode active learning algorithm for semantic segmentation which automatically selects important samples to be labeled for building a competitive classifier. In our approach the edge information of an image is first introduced as a new selecting clue of active learning, which can measure the essential information relevant to segmentation performance. In addition, we also incorporate the informativeness based on Query by Committee (QBC) and representativeness criteria in our algorithm. We combine three clues to select a batch of samples during each iteration. It is shown that the image edge information is significant for the active learning for semantic segmentation in the experiments. And we also demonstrate the performance of our method outperforms the state of the art active learning approaches on the datasets of CamVid, Stanford Background and PASCAL VOC 2012.