9 August 2018 Salient object extraction in low depth-of-field images using SVDD
Author Affiliations +
Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 1080604 (2018) https://doi.org/10.1117/12.2502896
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China
Abstract
Existing salient object extraction methods for the low depth-of-field (DOF) image are usually based on local saliency. However, in the low DOF image, the smooth region of salient objects is similar to the background in local saliency, so they are easily confused. In this paper, a novel salient object extraction method is proposed by introducing Support Vector Data Description (SVDD) for salient object shape description. It is the first time that SVDD is used for salient object extraction. SVDD makes full use of global characteristics of salient objects, which makes it possible for our approach to accurately extract salient objects containing smooth regions. Experiments on a Flickr dataset consisting of 141 low DOF images indicate that F-measure of our approach is better than the existing methods.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jupan Li, Jupan Li, Yupin Luo, Yupin Luo, } "Salient object extraction in low depth-of-field images using SVDD", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080604 (9 August 2018); doi: 10.1117/12.2502896; https://doi.org/10.1117/12.2502896
PROCEEDINGS
9 PAGES


SHARE
RELATED CONTENT

Remote logo detection using angle-distance histograms
Proceedings of SPIE (May 19 2016)
A review of salient region extraction
Proceedings of SPIE (August 19 2010)
Recognition Of Complex Graphical Objects
Proceedings of SPIE (March 02 1989)
Curvature-based shape representation of planar curves
Proceedings of SPIE (March 22 1996)
Image retrieval for information systems
Proceedings of SPIE (March 23 1995)

Back to Top