research-article

Depth Enhanced Saliency Detection Method

Authors:

Xiaochun CaoAuthors Info & Claims

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

Pages 23 - 27

https://doi.org/10.1145/2632856.2632866

Published: 10 July 2014 Publication History

Get Access

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.

References

[1]

R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk. Frequency-tuned salient region detection. In CVPR, pages 1597--1604, 2009.

Crossref

Google Scholar

[2]

Z. Chen, J. Yuan, and Y. Tan. Hybrid saliency detection for images. Signal Processing Letters, 20(1):95--98, 2013.

Crossref

Google Scholar

[3]

M. Cheng, G. Zhang, N. Mitra, X. Huang, and S. Hu. Global contrast based salient region detection. In CVPR, pages 409--416, 2011.

Digital Library

Google Scholar

[4]

H. Fu, X. Cao, and Z. Tu. Cluster-based co-saliency detection. IEEE Transactions on Image Processing, 22(10):3766--3778, 2013.

Digital Library

Google Scholar

[5]

L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. TPAMI, 20(11):1254--1259, 1998.

Digital Library

Google Scholar

[6]

C. Lang, T. Nguyen, H. Katti, K. Yadati, M. Kankanhalli, and S. Yan. Depth matters: Influence of depth cues on visual saliency. ECCV, pages 101--115, 2012.

Digital Library

Google Scholar

[7]

S. Liu, Y. Wang, L. Yuan, J. Bu, P. Tan, and J. Sun. Video stabilization with a depth camera. In CVPR, pages 89--95, 2012.

Digital Library

Google Scholar

[8]

T. Liu, Z. Yuan, J. Sun, N. Z. J. Wang, X. Tang, and H.-Y. Shum. Learning to detect a salient object. TPAMI, 33(2):353--367, 2011.

Digital Library

Google Scholar

[9]

Y. Niu, Y. Geng, X. Li, and F. Liu. Leveraging stereopsis for saliency analysis. In CVPR, pages 454--461, 2012.

Digital Library

Google Scholar

[10]

J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, and A. Blake. Real-time human pose recognition in parts from single depth images. In CVPR, pages 1297--1304, 2011.

Digital Library

Google Scholar

[11]

H. Simon and B. Richard. Kinecting the dots: Particle based scene flow from depth sensors. In ICCV, pages 2290--2295, 2011.

Digital Library

Google Scholar

[12]

J. Yan, M. Zhu, H. Liu, and Y. Liu. Visual saliency detection via sparsity pursuit. Signal Processing Letters, 17(8):739--742, 2010.

Crossref

Google Scholar

[13]

Q. Yan, L. Xu, J. Shi, and J. Jia. Hierarchical saliency detection. In CVPR, pages 1155--1162, 2013.

Digital Library

Google Scholar

[14]

C. Yang, L. Zhang, H. Lu, X. Ruan, and M.-H. Yang. Saliency detection via graph-based manifold ranking. In CVPR, pages 3166--3173, 2013.

Digital Library

Google Scholar

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
Show More Cited By

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

Recommendations

Depth-aware saliency detection using convolutional neural networks
Highlights
- We propose a depth-aware saliency model which consists of three networks.
- Color ...
Abstract
This paper proposes a new end-to-end depth-aware saliency model using three convolutional neural networks including color saliency network, depth saliency network and saliency fusion network, for saliency detection in RGBD images and ...
Object-level saliency detection with color attributes

Recently, saliency detection has been becoming a popular topic in computer vision. In this paper we propose an object-level saliency detection algorithm which explicitly explores bottom-up visual attention and objectness cues. Firstly, some category-...
Saliency detection for RGBD images
ICIMCS '15: Proceedings of the 7th International Conference on Internet Multimedia Computing and Service

Additional depth information from RGBD images is one of characteristics different from conventional 2D images. In this paper, we propose an effective saliency model to detect salient regions in RGBD images. Color contrast and depth contrast are first ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

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

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICIMCS '14

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

July 10 - 12, 2014

Xiamen, China

Acceptance Rates

Overall Acceptance Rate 163 of 456 submissions, 36%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

262
Total Citations
View Citations
873
Total Downloads

Downloads (Last 12 months)53
Downloads (Last 6 weeks)12

Reflects downloads up to 24 Nov 2024

Other Metrics

View Author Metrics

Citations

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
Xia CYang FDuan SGao XGe BLi KFang XZhang YKe Y(2024)ECW-EGNet: Exploring Cross-ModalWeighting and edge-guided decoder network for RGB-D salient object detectionComputer Science and Information Systems10.2298/CSIS231206022X21:3(947-969)Online publication date: 2024
https://doi.org/10.2298/CSIS231206022X
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
Liu NLuo ZZhang NHan J(2024)VST++: Efficient and Stronger Visual Saliency TransformerIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.338815346:11(7300-7316)Online publication date: Nov-2024
https://doi.org/10.1109/TPAMI.2024.3388153
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
https://doi.org/10.1109/TPAMI.2023.3324807
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
https://doi.org/10.1109/TNNLS.2022.3202241
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
https://dl.acm.org/doi/10.1109/TMM.2023.3301280
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
https://dl.acm.org/doi/10.1109/TMM.2023.3294003
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Depth-aware saliency detection using convolutional neural networks

Object-level saliency detection with color attributes

Saliency detection for RGBD images