Supervised Learning of Hierarchical Image Segmentation

We study the problem of predicting hierarchical image segmentations using supervised deep learning. While deep learning methods are now widely used as contour detectors, the lack of image datasets with hierarchical annotations has prevented researchers from explicitly training models to predict hierarchical contours. Image segmentation has been widely studied, but it is limited by only proposing a segmentation at a single scale. Hierarchical image segmentation solves this problem by proposing segmentation at multiple scales, capturing objects and structures at different levels of detail. However, this area of research appears to be less explored and therefore no hierarchical image segmentation dataset exists. In this paper, we provide a hierarchical adaptation of the Pascal-Part dataset [2], and use it to train a neural network for hierarchical image segmentation prediction. We demonstrate the efficiency of the proposed method through three benchmarks: the precision-recall and F-score benchmarks for boundary location, the level recovery fraction for assessing hierarchy quality, and the false discovery fraction. We show that our method successfully learns hierarchical boundaries in the correct order, and achieves better performance than the state-of-the-art model trained on single-scale segmentations.