BlumNet: Graph Component Detection for Object Skeleton Extraction

In this paper, we present a simple yet efficient framework, BlumNet, for extracting object skeletons in natural images and binary shapes. With the need for highly reliable skeletons in various multimedia applications, the proposed BlumNet is distinguished in three aspects: (1) The inception of graph decomposition and reconstruction strategies further simplifies the skeleton extraction task into a graph component detection problem, which significantly improves the accuracy and robustness of extracted skeletons. (2) The intuitive representation of each skeleton branch with multiple structured and overlapping line segments can effectively prevent the skeleton branch vanishing problem. (3) In comparison to traditional skeleton heatmaps, our approach directly outputs skeleton graphs, which is more feasible for real-world applications. Through comprehensive experiments, we demonstrate the advantages of BlumNet: significantly higher accuracy than the state-of-the-art AdaLSN (0.826 vs. 0.786) on the SK1491 dataset, a marked improvement in robustness on mixed object deformations, and also a state-of-the-art performance on binary shape datasets (e.g. 0.893 on the MPEG7 dataset).