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A Review of Co-Saliency Detection Algorithms: Fundamentals, Applications, and Challenges

Authors:

Xuelong LiAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 9, Issue 4

Article No.: 38, Pages 1 - 31

https://doi.org/10.1145/3158674

Published: 30 January 2018 Publication History

Abstract

Co-saliency detection is a newly emerging and rapidly growing research area in the computer vision community. As a novel branch of visual saliency, co-saliency detection refers to the discovery of common and salient foregrounds from two or more relevant images, and it can be widely used in many computer vision tasks. The existing co-saliency detection algorithms mainly consist of three components: extracting effective features to represent the image regions, exploring the informative cues or factors to characterize co-saliency, and designing effective computational frameworks to formulate co-saliency. Although numerous methods have been developed, the literature is still lacking a deep review and evaluation of co-saliency detection techniques. In this article, we aim at providing a comprehensive review of the fundamentals, challenges, and applications of co-saliency detection. Specifically, we provide an overview of some related computer vision works, review the history of co-saliency detection, summarize and categorize the major algorithms in this research area, discuss some open issues in this area, present the potential applications of co-saliency detection, and finally point out some unsolved challenges and promising future works. We expect this review to be beneficial to both fresh and senior researchers in this field and to give insights to researchers in other related areas regarding the utility of co-saliency detection algorithms.

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Cited By

Zhu JCai WZhang MLin YLiu M(2025)Saliency detection for underwater moving object with sonar based on motion estimation and multi-trajectory analysisPattern Recognition10.1016/j.patcog.2024.111043158(111043)Online publication date: Feb-2025
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Guan YQiu YLeng JYang FYu SLiu YFeng YZhu YZhou LLiang YZhang CLi CGuo MTsafrir DMUSUVATHI MGupta RAbu-Ghazaleh N(2024)Amanda: Unified Instrumentation Framework for Deep Neural NetworksProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 110.1145/3617232.3624864(1-18)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3617232.3624864
Chakraborty SNaha SBastan MK C ASamaras D(2024)Unsupervised and semi-supervised co-salient object detection via segmentation frequency statistics2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00040(331-341)Online publication date: 3-Jan-2024
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Index Terms

A Review of Co-Saliency Detection Algorithms: Fundamentals, Applications, and Challenges
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 9, Issue 4

Research Survey and Regular Papers

July 2018

280 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3183892

Editor:
Yu Zheng
Microsoft Research, China

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Copyright © 2018 ACM.

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Publication History

Published: 30 January 2018

Accepted: 01 November 2017

Revised: 01 October 2017

Received: 01 July 2017

Published in TIST Volume 9, Issue 4

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Zhu JCai WZhang MLin YLiu M(2025)Saliency detection for underwater moving object with sonar based on motion estimation and multi-trajectory analysisPattern Recognition10.1016/j.patcog.2024.111043158(111043)Online publication date: Feb-2025
https://doi.org/10.1016/j.patcog.2024.111043
Guan YQiu YLeng JYang FYu SLiu YFeng YZhu YZhou LLiang YZhang CLi CGuo MTsafrir DMUSUVATHI MGupta RAbu-Ghazaleh N(2024)Amanda: Unified Instrumentation Framework for Deep Neural NetworksProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 110.1145/3617232.3624864(1-18)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3617232.3624864
Chakraborty SNaha SBastan MK C ASamaras D(2024)Unsupervised and semi-supervised co-salient object detection via segmentation frequency statistics2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00040(331-341)Online publication date: 3-Jan-2024
https://doi.org/10.1109/WACV57701.2024.00040
Apostol ETruică CPaschke A(2024)ContCommRTD: A Distributed Content-Based Misinformation-Aware Community Detection System for Real-Time Disaster ReportingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.341723236:11(5811-5822)Online publication date: 1-Nov-2024
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Xiao HTang LLi BLuo ZLi S(2024)Zero-Shot Co-Salient Object Detection FrameworkICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10448084(4010-4014)Online publication date: 14-Apr-2024
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Zhu JGuo QJuefei-Xu FHuang YLiu YPu G(2024)Cosalpure: Learning Concept from Group Images for Robust Co-Saliency Detection2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00352(3669-3678)Online publication date: 16-Jun-2024
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Tan ZGu XCheng Q(2024)Co-saliency detection with two-stage co-attention mining and individual calibrationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107201127:PAOnline publication date: 1-Feb-2024
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Dong PWang BCong RSun HLi C(2024)Transformer with large convolution kernel decoder network for salient object detection in optical remote sensing imagesComputer Vision and Image Understanding10.1016/j.cviu.2023.103917240(103917)Online publication date: Mar-2024
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Mozaffari JAmirkhani AShokouhi S(2024)ColonGen: an efficient polyp segmentation system for generalization improvement using a new comprehensive datasetPhysical and Engineering Sciences in Medicine10.1007/s13246-023-01368-847:1(309-325)Online publication date: 15-Jan-2024
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