Boundary-aware RGBD salient object detection with cross-modal feature sampling

Publication Type

Journal Article

Publication Date

1-2020

Abstract

Mobile devices usually mount a depth sensor to resolve ill-posed problems, like salient object detection on cluttered background. The main barrier of exploring RGBD data is to handle the information from two different modalities. To cope with this problem, in this paper, we propose a boundary-aware cross-modal fusion network for RGBD salient object detection. In particular, to enhance the fusion of color and depth features, we present a cross-modal feature sampling module to balance the contribution of the RGB and depth features based on the statistics of their channel values. In addition, in our multi-scale dense fusion network architecture, we not only incorporate edge-sensitive losses to preserve the boundary of the detected salient region, but also refine its structure by merging the estimated saliency maps of different scales. We accomplish the multi-scale saliency map merging using two alternative methods which produce refined saliency maps via per-pixel weighted combination and an encoder-decoder network. Extensive experimental evaluations demonstrate that our proposed framework can achieve the state-of-the-art performance on several public RGBD-based datasets.

Keywords

Salient object detection, cross-modal, boundary-aware estimation

Discipline

Information Security

Research Areas

Information Systems and Management

Publication

IEEE Transactions on Image Processing

Volume

29

First Page

9496

Last Page

9507

ISSN

1057-7149

Identifier

10.1109/TIP.2020.3028170

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TIP.2020.3028170

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