Robust Salient Object Detection via Fusing Foreground and Background Priors

Automatic salient object detection without any supervised labor tends to greatly enhance many computer vision tasks. This paper proposes a novel bottom-up salient object detection framework which considers both foreground and background priors in detecting process. First, a series of foreground seed...

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Bibliographic Details
Published in2018 25th IEEE International Conference on Image Processing (ICIP) pp. 2341 - 2345
Main Authors Huang, Kan, Zhu, Chunbiao, Li, Ge
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2018
Subjects
Background prior
Estimation
Foreground prior
geodesic distance
Image color analysis
Image segmentation
Object detection
Principal component analysis
Robustness
Salient object detection
Task analysis
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Summary:Automatic salient object detection without any supervised labor tends to greatly enhance many computer vision tasks. This paper proposes a novel bottom-up salient object detection framework which considers both foreground and background priors in detecting process. First, a series of foreground seeds are extracted from an image based on surroundedness cue. Then, a foreground-corresponding saliency map is generated via ranking algorithm according to these seeds. In a similar way a series of background seeds are extracted and used for generating a background-corresponding saliency map. Finally, the two saliency maps are fused into one, and subsequently enhanced by geodesic refinement to derive the final saliency map. Extensive experimental evaluation demonstrates the effectiveness of our proposed framework against other outstanding methods.
ISSN:2381-8549
DOI:10.1109/ICIP.2018.8451046