By the same authors

From the same journal

The low-rank decomposition of correlation-enhanced superpixels for video segmentation

Research output: Contribution to journalArticlepeer-review

Full text download(s)

Published copy (DOI)



Publication details

JournalSoft Computing - A Fusion of Foundations, Methodologies and Applications
DateE-pub ahead of print - 22 Feb 2019
Number of pages11
Original languageEnglish


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.

Bibliographical note

© 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.

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations