research-article

Depth Enhanced Saliency Detection Method

Authors:

Yupeng Cheng,

Huazhu Fu,

Xingxing Wei,

Jiangjian Xiao, and

Xiaochun CaoAuthors Info & Claims

ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service

July 2014

Pages 23 - 27

https://doi.org/10.1145/2632856.2632866

Published: 10 July 2014 Publication History

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Abstract

Human vision system understands the environment from 3D perception. However, most existing saliency detection algorithms detect the salient foreground based on 2D image information. In this paper, we propose a saliency detection method using the additional depth information. In our method, saliency cues are provided to follow the laws of the visually salient stimuli in both color and depth spaces. Simultaneously, the 'center bias' is also extended to 'spatial' bias to represent the nature advantage in 3D image. In addition, We build a dataset to test our method and the experiments demonstrate that the depth information is useful for extracting the salient object from the complex scenes.

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Cited By

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Peng YZhai ZFeng M(2024)SLMSF-Net: A Semantic Localization and Multi-Scale Fusion Network for RGB-D Salient Object DetectionSensors10.3390/s2404111724:4(1117)Online publication date: 8-Feb-2024
https://doi.org/10.3390/s24041117
Xia CWang JGe B(2024)MLBSNet: Mutual Learning and Boosting Segmentation Network for RGB-D Salient Object DetectionElectronics10.3390/electronics1314269013:14(2690)Online publication date: 10-Jul-2024
https://doi.org/10.3390/electronics13142690
Yu MLiu JLiu YYan G(2024)Feature interaction and two-stage cross-modal fusion for RGB-D salient object detectionJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23322546:2(4543-4556)Online publication date: 14-Feb-2024
https://dl.acm.org/doi/10.3233/JIFS-233225
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Index Terms

Depth Enhanced Saliency Detection Method
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Interest point and salient region detections
      2. Computer vision tasks

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Information & Contributors

Information

Published In

ICIMCS '14: Proceedings of International Conference on Internet Multimedia Computing and Service

July 2014

430 pages

ISBN:9781450328104

DOI:10.1145/2632856

General Chairs:
Hanzi Wang
Xiamen University, China
,
Larry Davis
University of Maryland, USA
,
Program Chairs:
Wenwu Zhu
Tsinghua University, China
,
Stephan Kopf
University of Mannheim, Germany
,
Yanyun Qu
Xiamen University, China
,
Publications Chairs:
Jun Yu
Xiamen University, China
,
Jitao Sang
Institute of Automation, CAS, China
,
Tao Mei
Microsoft Research Asia, China

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In-Cooperation

NSF of China: National Natural Science Foundation of China
Beijing ACM SIGMM Chapter

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 July 2014

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Conference

ICIMCS '14

ICIMCS '14: International Conference on Internet Multimedia Computing and Service

July 10 - 12, 2014

Xiamen, China

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Overall Acceptance Rate 163 of 456 submissions, 36%

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Cited By

View all

Peng YZhai ZFeng M(2024)SLMSF-Net: A Semantic Localization and Multi-Scale Fusion Network for RGB-D Salient Object DetectionSensors10.3390/s2404111724:4(1117)Online publication date: 8-Feb-2024
https://doi.org/10.3390/s24041117
Xia CWang JGe B(2024)MLBSNet: Mutual Learning and Boosting Segmentation Network for RGB-D Salient Object DetectionElectronics10.3390/electronics1314269013:14(2690)Online publication date: 10-Jul-2024
https://doi.org/10.3390/electronics13142690
Yu MLiu JLiu YYan G(2024)Feature interaction and two-stage cross-modal fusion for RGB-D salient object detectionJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23322546:2(4543-4556)Online publication date: 14-Feb-2024
https://dl.acm.org/doi/10.3233/JIFS-233225
Gao HSu YWang FLi H(2024)Heterogeneous Fusion and Integrity Learning Network for RGB-D Salient Object DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365647620:7(1-24)Online publication date: 5-Apr-2024
https://dl.acm.org/doi/10.1145/3656476
Li LHan JLiu NKhan SCholakkal HAnwer RKhan F(2024)Robust Perception and Precise Segmentation for Scribble-Supervised RGB-D Saliency DetectionIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.332480746:1(479-496)Online publication date: Jan-2024
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Chen QZhang ZLu YFu KZhao Q(2024)3-D Convolutional Neural Networks for RGB-D Salient Object Detection and BeyondIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.320224135:3(4309-4323)Online publication date: Mar-2024
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Yu NShi HHan Y(2024)Joint Correcting and Refinement for Balanced Low-Light Image EnhancementIEEE Transactions on Multimedia10.1109/TMM.2023.334833326(6310-6324)Online publication date: 2024
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Xiao FPu ZChen JGao X(2024)DGFNet: Depth-Guided Cross-Modality Fusion Network for RGB-D Salient Object DetectionIEEE Transactions on Multimedia10.1109/TMM.2023.330128026(2648-2658)Online publication date: 1-Jan-2024
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Sun FRen PYin BWang FLi H(2024)CATNet: A Cascaded and Aggregated Transformer Network for RGB-D Salient Object DetectionIEEE Transactions on Multimedia10.1109/TMM.2023.329400326(2249-2262)Online publication date: 1-Jan-2024
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He ZShao FChen GChai XHo Y(2024)SCFANet: Semantics and Context Feature Aggregation Network for 360° Salient Object DetectionIEEE Transactions on Multimedia10.1109/TMM.2023.329399426(2276-2288)Online publication date: 2024
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