Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/83156
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Type: Conference paper
Title: Contextual hypergraph modeling for salient object detection
Author: Li, X.
Li, Y.
Shen, C.
Dick, A.
Van Den Hengel, A.
Citation: Proceedings 2013 IEEE International Conference on Computer Vision, ICCV 2013, Sydney, NSW, Australia, 1-8 December 2013: pp.3328-3335
Publisher: IEEE Computer Society
Publisher Place: USA
Issue Date: 2013
Series/Report no.: IEEE International Conference on Computer Vision
ISBN: 9781479928392
ISSN: 1550-5499
Conference Name: International Conference on Computer Vision (2013 : Sydney)
Statement of
Responsibility: 
Xi Li, Yao Li, Chunhua Shen, Anthony Dick, Anton van den Hengel
Abstract: Salient object detection aims to locate objects that capture human attention within images. Previous approaches often pose this as a problem of image contrast analysis. In this work, we model an image as a hypergraph that utilizes a set of hyperedges to capture the contextual properties of image pixels or regions. As a result, the problem of salient object detection becomes one of finding salient vertices and hyperedges in the hypergraph. The main advantage of hypergraph modeling is that it takes into account each pixel’s (or region’s) affinity with its neighborhood as well as its separation from image background. Furthermore, we propose an alternative approach based on centerversus- surround contextual contrast analysis, which performs salient object detection by optimizing a cost-sensitive support vector machine (SVM) objective function. Experimental results on four challenging datasets demonstrate the effectiveness of the proposed approaches against the stateof- the-art approaches to salient object detection.
Rights: © 2013 IEEE
RMID: 0020137319
DOI: 10.1109/ICCV.2013.413
Appears in Collections:Computer Science publications

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