Combining external prior and internal features: toward a robust foreground seed selection method

Huibin Wang; Chao Zhu; Lizhong Xu; Zhen Zhang; Yunbo Xiong

doi:10.1117/1.JEI.28.3.033032

22 June 2019 Combining external prior and internal features: toward a robust foreground seed selection method

Huibin Wang, Chao Zhu, Lizhong Xu, Zhen Zhang, Yunbo Xiong

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 3, 033032 (June 2019). https://doi.org/10.1117/1.JEI.28.3.033032

Abstract

Graph-based salient object detection has been widely applied in many applications, because of its excellent performance and strong theoretical basis. Basically, the performance of this type of methods depends on the correctness in foreground seed selection. In research aiming to exactly identify the seeds on foreground objects, an external prior has been defined in recent work as having an image boundary that is mostly background (called boundary prior), so the foreground seeds must locate around the image center. However, this is not the case when salient objects are spatially close to the image boundary. This problem will cause a severe error in salient object detection, because background noises are likely mixed in foreground seeds. To solve this problem, we propose a robust foreground seed selection method for salient object detection. In our method, the external prior and multiple internal image features are combined for foreground seed selection. Our method can relax the limitation of the external prior and make the foreground seed selection more adaptive and robust to diverse samples. As a result, the proposed method can generate satisfying results, no matter where the salient object is located. This advantage is demonstrated by experimental comparisons with several state-of-art methods.

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Huibin Wang, Chao Zhu, Lizhong Xu, Zhen Zhang, and Yunbo Xiong "Combining external prior and internal features: toward a robust foreground seed selection method," Journal of Electronic Imaging 28(3), 033032 (22 June 2019). https://doi.org/10.1117/1.JEI.28.3.033032

Received: 4 March 2019; Accepted: 3 June 2019; Published: 22 June 2019

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KEYWORDS

Image processing

Magnetic resonance imaging

Databases

Principal component analysis

Absorption

Chaos

Feature extraction

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