Batch Mode Active Learning with Nonlocal Self-Similarity Prior for Semantic Segmentation

Semantic segmentation is a task that heavily relies on the annotated data. The image annotation cost is very expensive. While active learning aims to select the most valuable samples by an iterative procedure. It can reduce the annotation cost and improve the performance of classification. In semantic segmentation, its more common to select a batch of instances instead of a single instance at each iteration. In this paper, we propose a novel batch mode active learning algorithm for semantic segmentation. Different from the previous active learning algorithms for the image classification, we first introduce a new selecting criterion : the image prior of nonlocal self-similarity. It can measure the interaction between the pixels of the image. We combine the informativeness and representativeness with the criterion of nonlocal self-similarity to complete the selection of images at each iteration. In addition, we also use the model uncertainty to measure the information of samples. The model uncertainty is captured by using Monte Carlo Dropout in the semantic segmentation model. In this work, we evaluate our method on the CamVid and PASCAL VOC 2012 datasets. The importance of the nonlocal self-similarity is also assessed. The experiments demonstrate that our algorithm outperforms current state-of-the-art active learning methods over the segmentation performance.