Salient Object Detection Based on Weighted Hypergraph and Random Walk
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-23
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Recently, salient object detection based on the graph model has attracted extensive research interest in computer vision because the graph model can represent the relationship between two regions better.
However, it is difficult to capture the high-level relationship between multiple regions.
In this algorithm, the input image is segmented into superpixels first.
Then, a weighted hypergraph model is established using fuzzy C-means clustering algorithm and a new weighting strategy.
Finally, the random walk algorithm is used to sort all superpixels on the weighted hypergraph model to obtain the salient object.
The experimental results on three benchmark datasets demonstrate that the proposed method performs better than some other state-of-the-art methods.
American Psychological Association (APA)
Wei, WeiYi& Chen, Hui. 2020. Salient Object Detection Based on Weighted Hypergraph and Random Walk. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1193680
Modern Language Association (MLA)
Wei, WeiYi& Chen, Hui. Salient Object Detection Based on Weighted Hypergraph and Random Walk. Mathematical Problems in Engineering No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1193680
American Medical Association (AMA)
Wei, WeiYi& Chen, Hui. Salient Object Detection Based on Weighted Hypergraph and Random Walk. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1193680
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1193680