Fast segmentation for large and sparsely labeled coral images

Abstract

Listen

Translate article

Marine organism datasets often present sparse annotated labels and with many objects in cluttered background. Therefore, there are two challenges to do image segmentation on these sparsely labeled datasets: one is to obtain denser labeled training data and the other is to improve the speed of testing on large images. In this paper, we propose a label augmentation method to generate more labels for training based on the superpixel algorithm, and we also create coarse-to-fine approach to detect the coral areas quickly in the large images. Our experiments run on coral image dataset collected in Pulley Ridge1, proving that this label augmentation and coarse-to-fine approach allows us to speed up the process of quantifying the percent of corals in large images while preserving accuracy.