Density-based one-shot active learning for image segmentation

Abstract

Image segmentation is a key step in image processing tasks, which has significant applications in computer vision field such as medical image analysis, scene understanding and video monitoring, etc. However, image segmentation tasks usually require a large number of labeled samples to obtain great performance of convolutional neural networks (CNNs). Active learning (AL) can select valuable samples for annotation, so as to reduce the annotation cost as much as possible while maintaining the performance of CNNs. Further, one-shot AL can select valuable samples by once, which eliminates the need for iterative sample selection and annotation. However, existing one-shot AL approaches extremely rely on complex clustering algorithm, which brings a limitation in practice, i.e., we often do not know how to set the hyperparameters. In this paper, we propose a clustering-free one-shot AL framework, which is based on self-supervised feature learning and density-based query strategy. Our framework can select samples with high local density robustly against hyperparameters. The experimental results are impressive that state-of-the-art one-shot active learning performance can be achieved with simple density-based sampling.