4.7 Article

BiconNet: An edge-preserved connectivity-based approach for salient object detection

Journal

PATTERN RECOGNITION
Volume 121, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2021.108231

Keywords

Salient object detection; Visual saliency; Connectivity modeling; Deep learning; Edge modeling

Funding

  1. National Institutes of Health [R01 EY030124, P30EY005722]
  2. Foundation Fighting Blindness [BR-CL-0621-0812-DUKE]
  3. Hartwell Foundation Postdoctoral Fellowship
  4. Research to Prevent Blindness

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Traditional deep learning-based methods view salient object detection as a pixel-wise saliency modeling task, but often lack sufficient utilization of inter-pixel information. To address this limitation, a connectivity-based approach called bilateral connectivity network (BiconNet) is proposed, which has been demonstrated to effectively model inter-pixel relationships and object saliency through comprehensive experiments.
Salient object detection (SOD) is viewed as a pixel-wise saliency modeling task by traditional deep learning-based methods. A limitation of current SOD models is insufficient utilization of inter-pixel information, which usually results in imperfect segmentation near edge regions and low spatial coherence. As we demonstrate, using a saliency mask as the only label is suboptimal. To address this limitation, we propose a connectivity-based approach called bilateral connectivity network (BiconNet), which uses connectivity masks together with saliency masks as labels for effective modeling of inter-pixel relationships and object saliency. Moreover, we propose a bilateral voting module to enhance the output connectivity map, and a novel edge feature enhancement method that efficiently utilizes edge-specific features. Through comprehensive experiments on five benchmark datasets, we demonstrate that our proposed method can be plugged into any existing state-of-the-art saliency-based SOD framework to improve its performance with negligible parameter increase. (c) 2021 Elsevier Ltd. All rights reserved.

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