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Co-Saliency Detection via Base Reconstruction

Authors:

Xiaochun Cao,

Yupeng Cheng,

Zhiqiang Tao,

Huazhu FuAuthors Info & Claims

MM '14: Proceedings of the 22nd ACM international conference on Multimedia

Pages 997 - 1000

https://doi.org/10.1145/2647868.2655007

Published: 03 November 2014 Publication History

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Abstract

Co-saliency aims at detecting common saliency in a series of images, which is useful for a variety of multimedia applications. In this paper, we address the co-saliency detection to a reconstruction problem: the foreground could be well reconstructed by using the reconstruction bases, which are extracted from each image and have the similar appearances in the feature space. We firstly obtain a candidate set by measuring the saliency prior of each image. Relevance information among the multiple images is utilized to remove the inaccuracy reconstruction bases. Finally, with the updated reconstruction bases, we rebuild the images and provide the reconstruction error regarded as a negative correlational value in co-saliency measurement. The satisfactory quantitative and qualitative experimental results on two benchmark datasets demonstrate the efficiency and effectiveness of our method.

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Cited By

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Ge YPan JRen JHe MBi HZhang Q(2025)Co-salient object detection with consensus mining and consistency cross-layer interactive decodingImage and Vision Computing10.1016/j.imavis.2025.105414154(105414)Online publication date: Feb-2025
https://doi.org/10.1016/j.imavis.2025.105414
Zhang CBi HXiang TWu RTong JWang X(2024)Collaborative Camouflaged Object Detection: A Large-Scale Dataset and BenchmarkIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.331709135:12(18470-18484)Online publication date: Dec-2024
https://doi.org/10.1109/TNNLS.2023.3317091
Tong JBi YZhang CBi HYuan Y(2024)Local to global purification strategy to realize collaborative camouflaged object detectionComputer Vision and Image Understanding10.1016/j.cviu.2024.103932241(103932)Online publication date: Apr-2024
https://doi.org/10.1016/j.cviu.2024.103932
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Index Terms

Co-Saliency Detection via Base Reconstruction
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Interest point and salient region detections

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Information

Published In

MM '14: Proceedings of the 22nd ACM international conference on Multimedia

November 2014

1310 pages

ISBN:9781450330633

DOI:10.1145/2647868

General Chairs:
Kien A. Hua
University of Central Florida, USA
,
Yong Rui
Microsoft Research, China
,
Ralf Steinmetz
Technische Universitt Darmstadt, Germany
,
Program Chairs:
Alan Hanjalic
Delft University of Technology, Netherlands
,
Apostol (Paul) Natsev
Google, USA
,
Wenwu Zhu
Tsinghua University, China

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Association for Computing Machinery

New York, NY, United States

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Published: 03 November 2014

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MM '14: 2014 ACM Multimedia Conference

November 3 - 7, 2014

Florida, Orlando, USA

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MM '14 Paper Acceptance Rate 55 of 286 submissions, 19%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Ge YPan JRen JHe MBi HZhang Q(2025)Co-salient object detection with consensus mining and consistency cross-layer interactive decodingImage and Vision Computing10.1016/j.imavis.2025.105414154(105414)Online publication date: Feb-2025
https://doi.org/10.1016/j.imavis.2025.105414
Zhang CBi HXiang TWu RTong JWang X(2024)Collaborative Camouflaged Object Detection: A Large-Scale Dataset and BenchmarkIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.331709135:12(18470-18484)Online publication date: Dec-2024
https://doi.org/10.1109/TNNLS.2023.3317091
Tong JBi YZhang CBi HYuan Y(2024)Local to global purification strategy to realize collaborative camouflaged object detectionComputer Vision and Image Understanding10.1016/j.cviu.2024.103932241(103932)Online publication date: Apr-2024
https://doi.org/10.1016/j.cviu.2024.103932
Sun XZhang XXu CXiao MTang Y(2023)Tensorial Multiview Representation for Saliency Detection via Nonconvex ApproachIEEE Transactions on Cybernetics10.1109/TCYB.2021.313903753:3(1816-1829)Online publication date: Mar-2023
https://doi.org/10.1109/TCYB.2021.3139037
Agrawal AKumari TSoniya MMeenakshi SJegadeesan RAhmad S(2023)A Comprehensive Analysis on Co-Saliency Detection on Learning Approaches2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM)10.1109/ICIPTM57143.2023.10118121(1-6)Online publication date: 22-Feb-2023
https://doi.org/10.1109/ICIPTM57143.2023.10118121
Ge YZhang QXiang TZhang CZhang JBi H(2023)GSNNet: Group semantic-guided neighbor interaction network for co-salient object detectionComputer Vision and Image Understanding10.1016/j.cviu.2022.103611227(103611)Online publication date: Jan-2023
https://doi.org/10.1016/j.cviu.2022.103611
Li BTang LKuang SSong MDing S(2022)Toward Stable Co-Saliency Detection and Object Co-SegmentationIEEE Transactions on Image Processing10.1109/TIP.2022.321290631(6532-6547)Online publication date: 2022
https://doi.org/10.1109/TIP.2022.3212906
Zhang LWu H(2022)Cosaliency Detection and Region-of-Interest Extraction via Manifold Ranking and MRF in Remote Sensing ImagesIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2021.307944160(1-17)Online publication date: 2022
https://doi.org/10.1109/TGRS.2021.3079441
Tang LLi BKuang SSong MDing S(2022)Re-Thinking the Relations in Co-Saliency DetectionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2022.315092332:8(5453-5466)Online publication date: Aug-2022
https://doi.org/10.1109/TCSVT.2022.3150923
Korczakowski JSarwas GCzajewski W(2022)CoU2Net and CoLDF: Two Novel Methods Built on Basis of Double-Branch Co-Salient Object Detection FrameworkIEEE Access10.1109/ACCESS.2022.319775210(84989-85001)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3197752
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Co-saliency Detection with Graph Matching

Co-saliency detection via inter and intra saliency propagation

RGBD co-saliency detection via multiple kernel boosting and fusion

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