research-article

Sparse Graph Connectivity for Image Segmentation

Authors:

Yang YangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 4

Article No.: 46, Pages 1 - 19

https://doi.org/10.1145/3397188

Published: 16 June 2020 Publication History

Abstract

It has been demonstrated that the segmentation performance is highly dependent on both subspace preservation and graph connectivity. In the literature, the full connectivity method linearly represents each data point (e.g., a pixel in one image) by all data points for achieving subspace preservation, while the sparse connectivity method was designed to linearly represent each data point by a set of data points for achieving graph connectivity. However, previous methods only focused on either subspace preservation or graph connectivity. In this article, we propose a Sparse Graph Connectivity (SGC) method for image segmentation to automatically learn the affinity matrix from the low-dimensional space of original data, which aims at simultaneously achieving subspace preservation and graph connectivity. To do this, the proposed SGC simultaneously learns a self-representation affinity matrix for subspace preservation and a sparse affinity matrix for graph connectivity, from the intrinsic low-dimensional feature space of high-dimensional original data. Meanwhile, the self-representation affinity matrix is pushed to be similar to the sparse affinity as well as be the final segmentation results. Experimental result on synthetic and real-image datasets showed that our SGC method achieved the best segmentation performance, compared to state-of-the-art segmentation methods.

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Cited By

Cai XZhang RWu C(2024)Robust subspace clustering image segmentation algorithm based on noise suppression2024 6th International Conference on Natural Language Processing (ICNLP)10.1109/ICNLP60986.2024.10692555(552-559)Online publication date: 22-Mar-2024
https://doi.org/10.1109/ICNLP60986.2024.10692555
Hou MLv WKong MLi RLiu ZWang DWang JChen Y(2023)Efficient predictor of pressurized water reactor safety parameters by topological information embedded convolutional neural networkAnnals of Nuclear Energy10.1016/j.anucene.2023.110004192(110004)Online publication date: Nov-2023
https://doi.org/10.1016/j.anucene.2023.110004
Francis JBaburaj MGeorge S(2022)An l½ and Graph Regularized Subspace Clustering Method for Robust Image SegmentationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/347651418:2(1-24)Online publication date: 16-Feb-2022
https://dl.acm.org/doi/10.1145/3476514
Show More Cited By

Index Terms

Sparse Graph Connectivity for Image Segmentation
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 14, Issue 4

August 2020

316 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3403605

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

Issue’s Table of Contents

Copyright © 2020 ACM.

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Publication History

Published: 16 June 2020

Online AM: 07 May 2020

Accepted: 01 April 2020

Revised: 01 February 2020

Received: 01 October 2019

Published in TKDD Volume 14, Issue 4

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Natural Science Foundation of China
Research Fund of Guangxi Key Lab of Multisource Information Mining and Security
Project of Guangxi Science and Technology
Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing
Guangxi “Bagui” Teams for Innovation and Research
Marsden Fund of New Zealand

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Cited By

Cai XZhang RWu C(2024)Robust subspace clustering image segmentation algorithm based on noise suppression2024 6th International Conference on Natural Language Processing (ICNLP)10.1109/ICNLP60986.2024.10692555(552-559)Online publication date: 22-Mar-2024
https://doi.org/10.1109/ICNLP60986.2024.10692555
Hou MLv WKong MLi RLiu ZWang DWang JChen Y(2023)Efficient predictor of pressurized water reactor safety parameters by topological information embedded convolutional neural networkAnnals of Nuclear Energy10.1016/j.anucene.2023.110004192(110004)Online publication date: Nov-2023
https://doi.org/10.1016/j.anucene.2023.110004
Francis JBaburaj MGeorge S(2022)An l½ and Graph Regularized Subspace Clustering Method for Robust Image SegmentationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/347651418:2(1-24)Online publication date: 16-Feb-2022
https://dl.acm.org/doi/10.1145/3476514
Chen BZhang ZLi YLu GZhang D(2022)Multi-Label Chest X-Ray Image Classification via Semantic Similarity Graph EmbeddingIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2021.307990032:4(2455-2468)Online publication date: Apr-2022
https://doi.org/10.1109/TCSVT.2021.3079900
Xu YLi Q(2021)Music Classification and Detection of Location Factors of Feature Words in Complex Noise EnvironmentComplexity10.1155/2021/55189672021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/5518967

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