Revisiting Feature Fusion for RGB-T Salient Object Detection

Qiang Zhang, Tonglin Xiao, Nianchang Huang, Dingwen Zhang, Jungong Han

Research output: Contribution to journalArticlepeer-review

74 Citations (SciVal)
415 Downloads (Pure)

Abstract

While many RGB-based saliency detection algorithms have recently shown the capability of segmenting salient objects from an image, they still suffer from unsatisfactory performance when dealing with complex scenarios, insufficient illumination or occluded appearances. To overcome this problem, this article studies RGB-T saliency detection, where we take advantage of thermal modality's robustness against illumination and occlusion. To achieve this goal, we revisit feature fusion for mining intrinsic RGB-T saliency patterns and propose a novel deep feature fusion network, which consists of the multi-scale, multi-modality, and multi-level feature fusion modules. Specifically, the multi-scale feature fusion module captures rich contexture features from each modality feature, while the multi-modality and multi-level feature fusion modules integrate complementary features from different modality features and different level of features, respectively. To demonstrate the effectiveness of the proposed approach, we conduct comprehensive experiments on the RGB-T saliency detection benchmark. The experimental results demonstrate that our approach outperforms other state-of-the-art methods and the conventional feature fusion modules by a large margin.

Original languageEnglish
Article number9161021
Pages (from-to)1804-1818
Number of pages15
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume31
Issue number5
Early online date06 Aug 2020
DOIs
Publication statusPublished - 01 May 2021

Keywords

  • RGB-T
  • Salient object detection
  • feature fusion
  • multi-level
  • multi-modality
  • multi-scale

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