Xu, Haixia, Hancock, Edwin R. orcid.org/0000-0003-4496-2028 and Zhou, Wei (2019) The low-rank decomposition of correlation-enhanced superpixels for video segmentation. Soft Computing - A Fusion of Foundations, Methodologies and Applications. ISSN 1432-7643
Abstract
Low-rank decomposition (LRD) is an effective scheme to explore the affinity among superpixels in the image and video segmentation. However, the superpixel feature collected based on colour, shape, and texture may be rough, incompatible, and even conflicting if multiple features extracted in various manners are vectored and stacked straight together. It poses poor correlation, inconsistence on intra-category superpixels, and similarities on inter-category superpixels. This paper proposes a correlation-enhanced superpixel for video segmentation in the framework of LRD. Our algorithm mainly consists of two steps, feature analysis to establish the initial affinity among superpixels, followed by construction of a correlation-enhanced superpixel. This work is very helpful to perform LRD effectively and find the affinity accurately and quickly. Experiments conducted on datasets validate the proposed method. Comparisons with the state-of-the-art algorithms show higher speed and more precise in video segmentation.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer-Verlag GmbH Germany, part of Springer Nature 2019. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 27 Feb 2019 12:50 |
Last Modified: | 16 Oct 2024 15:31 |
Published Version: | https://doi.org/10.1007/s00500-019-03849-z |
Status: | Published online |
Refereed: | Yes |
Identification Number: | 10.1007/s00500-019-03849-z |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:143051 |