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Salient object detection using a covariance-based CNN model in low-contrast images

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Abstract

Salient object detection model with active environment perception can substantially facilitate a wide range of applications. Conventional models primarily rely on handcrafted low-level image features or high-level features. However, these models may face great challenges in low-lighting scenario, due to the lack of well-defined features to represent saliency information in low-contrast images. In this paper, we propose a novel deep neural network framework embedded with covariance descriptor for salient object detection in low-contrast images. Several low-level features are extracted to compute their mutual covariance, which is then trained via a 7-layer convolutional neural network (CNN). The saliency map can be generated by estimating the saliency score of each region via the pre-trained CNN model. Extensive experiments have been conducted on six challenging datasets to evaluate the performance of the proposed model against ten state-of-the-art models.

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Acknowledgements

This work was supported by the Natural Science Foundation of China (61602349, 61373109, and 61273225) and the China Scholarship Council (201508420248).

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, China

    Nan Mu, Xin Xu, Xiaolong Zhang & Hong Zhang

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, Wuhan, 430065, China

    Xin Xu, Xiaolong Zhang & Hong Zhang

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  1. Nan Mu
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  2. Xin Xu
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  3. Xiaolong Zhang
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  4. Hong Zhang
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Correspondence to Xin Xu.

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Mu, N., Xu, X., Zhang, X. et al. Salient object detection using a covariance-based CNN model in low-contrast images. Neural Comput & Applic 29, 181–192 (2018). https://doi.org/10.1007/s00521-017-2870-6

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