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RGB-T salient object detection via fusing multi-level CNN features
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Zhang, Qiang, Huang, Nianchang, Yao, Lin, Zhang, Dingwen, Shan, Caifeng and Han, Jungong (2019) RGB-T salient object detection via fusing multi-level CNN features. IEEE Transactions on Image Processing . p. 1. doi:10.1109/TIP.2019.2959253 (In Press)
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WRAP-RGB-T-salient-object-detection-fusing-Han-2020.pdf - Accepted Version - Requires a PDF viewer. Download (5Mb) | Preview |
Official URL: http://dx.doi.org/10.1109/TIP.2019.2959253
Abstract
RGB-induced salient object detection has recently witnessed substantial progress, which is attributed to the superior feature learning capability of deep convolutional neural networks (CNNs). However, such detections suffer from challenging scenarios characterized by cluttered backgrounds, low-light conditions and variations in illumination. Instead of improving RGB based saliency detection, this paper takes advantage of the complementary benefits of RGB and thermal infrared images. Specifically, we propose a novel end-to-end network for multi-modal salient object detection, which turns the challenge of RGB-T saliency detection to a CNN feature fusion problem. To this end, a backbone network (e.g., VGG-16) is first adopted to extract the coarse features from each RGB or thermal infrared image individually, and then several adjacent-depth feature combination (ADFC) modules are designed to extract multi-level refined features for each single-modal input image, considering that features captured at different depths differ in semantic information and visual details. Subsequently, a multi-branch group fusion (MGF) module is employed to capture the cross-modal features by fusing those features from ADFC modules for a RGB-T image pair at each level. Finally, a joint attention guided bi-directional message passing (JABMP) module undertakes the task of saliency prediction via integrating the multi-level fused features from MGF modules. Experimental results on several public RGB-T salient object detection datasets demonstrate the superiorities of our proposed algorithm over the state-of-the-art approaches, especially under challenging conditions, such as poor illumination, complex background and low contrast.
| Item Type: | Journal Article | |||||||||||||||
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| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) | |||||||||||||||
| Divisions: | Faculty of Science > WMG (Formerly the Warwick Manufacturing Group) | |||||||||||||||
| Library of Congress Subject Headings (LCSH): | Image analysis, Pattern recognition systems, Neural networks (Computer science), Image analysis -- Data processing -- Research | |||||||||||||||
| Journal or Publication Title: | IEEE Transactions on Image Processing | |||||||||||||||
| Publisher: | IEEE | |||||||||||||||
| ISSN: | 1057-7149 | |||||||||||||||
| Official Date: | 17 December 2019 | |||||||||||||||
| Dates: |
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| Date of first compliant deposit: | 20 January 2020 | |||||||||||||||
| Page Range: | p. 1 | |||||||||||||||
| DOI: | 10.1109/TIP.2019.2959253 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | In Press | |||||||||||||||
| Publisher Statement: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
| RIOXX Funder/Project Grant: |
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