DeepFlux for Skeleton Detection in the Wild

Abstract

The medial axis, or skeleton, is a fundamental object representation that has been extensively used in shape recognition. Yet, its extension to natural images has been challenging due to the large appearance and scale variations of objects and complex background clutter that appear in this setting. In contrast to recent methods that address skeleton extraction as a binary pixel classification problem, in this article we present an alternative formulation for skeleton detection. We follow the spirit of flux-based algorithms for medial axis recovery by training a convolutional neural network to predict a two-dimensional vector field encoding the flux representation. The skeleton is then recovered from the flux representation, which captures the position of skeletal pixels relative to semantically meaningful entities (e.g., image points in spatial context, and hence the implied object boundaries), resulting in precise skeleton detection. Moreover, since the flux representation is a region-based vector field, it is better able to cope with object parts of large width. We evaluate the proposed method, termed DeepFlux, on six benchmark datasets, consistently achieving superior performance over state-of-the-art methods. Finally, we demonstrate an application of DeepFlux, augmented with a skeleton scale estimation module, to detect objects in aerial images. This combination yields results that are competitive with models trained specifically for object detection, showcasing the versatility and effectiveness of mid-level representations in high-level tasks. An implementation of our method is available at https://github.com/YukangWang/DeepFlux .