4.6 Article

Improving random walker segmentation using a nonlocal bipartite graph

Journal

BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Volume 71, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2021.103154

Keywords

Random walker segmentation; Bipartite graph segmentation; Superpixel segmentation

Funding

  1. National Natural Science Foundation of China [62172438, 61502005]
  2. fundamental research funds for the central universities [31732111303, 31512111310]
  3. State Key Laboratory for Novel Software Technology, Nanjing University [KFKT2019B17]

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The proposed method addresses the issue of distant repetitive patterns in object segmentation by introducing nonlocal affinity and a multi-scale superpixel bipartite graph, which is demonstrated to be effective through extensive experiments on multiple datasets.
The classical random walk segmentation explores merely local affinity among neighboring pixels for cutting out objects, which falls short of effectiveness when handling distant repetitive patterns. Meanwhile, the running efficiency is also limited by solving a large scale linear system. To alleviate the quandary, in this paper, we first propose to introduce nonlocal affinity among distant pixels with similar local features in the underlying segmentation graph, which enables label propagation among disconnected foregrounds and thus multiple repetitive patterns can be segmented jointly. Secondly, the segmentation graph is extended to a multi-scale superpixel based bipartite graph, where random walker segmentation is performed to transfer labeling information from annotated seeds to superpixels and further to unlabeled pixels. We show such a bipartite graph based approach can considerably save the computational cost without sacrificing the segmentation accuracy. Extensive experiments are conducted on multiple datasets, demonstrating the effectiveness of the proposed method.

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