Saliency Detection Based on the Combination of High-Level Knowledge and Low-Level Cues in Foggy Images
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School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China
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Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430065, China
3
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(4), 374; https://doi.org/10.3390/e21040374
Received: 28 February 2019
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Revised: 1 April 2019
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Accepted: 3 April 2019
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Published: 6 April 2019
(This article belongs to the Special Issue Statistical Machine Learning for Human Behaviour Analysis)
A key issue in saliency detection of the foggy images in the wild for human tracking is how to effectively define the less obvious salient objects, and the leading cause is that the contrast and resolution is reduced by the light scattering through fog particles. In this paper, to suppress the interference of the fog and acquire boundaries of salient objects more precisely, we present a novel saliency detection method for human tracking in the wild. In our method, a combination of object contour detection and salient object detection is introduced. The proposed model can not only maintain the object edge more precisely via object contour detection, but also ensure the integrity of salient objects, and finally obtain accurate saliency maps of objects. Firstly, the input image is transformed into HSV color space, and the amplitude spectrum (AS) of each color channel is adjusted to obtain the frequency domain (FD) saliency map. Then, the contrast of the local-global superpixel is calculated, and the saliency map of the spatial domain (SD) is obtained. We use Discrete Stationary Wavelet Transform (DSWT) to fuse the cues of the FD and SD. Finally, a fully convolutional encoder–decoder model is utilized to refine the contour of the salient objects. Experimental results demonstrate that the presented model can remove the influence of fog efficiently, and the performance is better than 16 state-of-the-art saliency models.
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Keywords:
saliency detection; foggy image; spatial domain; frequency domain; object contour detection; discrete stationary wavelet transform
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MDPI and ACS Style
Zhu, X.; Xu, X.; Mu, N. Saliency Detection Based on the Combination of High-Level Knowledge and Low-Level Cues in Foggy Images. Entropy 2019, 21, 374. https://doi.org/10.3390/e21040374
AMA Style
Zhu X, Xu X, Mu N. Saliency Detection Based on the Combination of High-Level Knowledge and Low-Level Cues in Foggy Images. Entropy. 2019; 21(4):374. https://doi.org/10.3390/e21040374
Chicago/Turabian StyleZhu, Xin, Xin Xu, and Nan Mu. 2019. "Saliency Detection Based on the Combination of High-Level Knowledge and Low-Level Cues in Foggy Images" Entropy 21, no. 4: 374. https://doi.org/10.3390/e21040374
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