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Hierarchical salient object detection model using contrast-based saliency and color spatial distribution

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Abstract

Visual saliency is an important cue in human visual system to detect salient objects in natural scenes. It has attracted a lot of research focus in computer vision, and has been widely used in many applications including image retrieval, object recognition, image segmentation, and etc. However, the accuracy of salient object detection model remains a challenge. Accordingly, a hierarchical salient object detection model is presented in this paper. In order to accurately interpret object saliency in image, we propose to investigate distinctive features from a global perspective. Image contrast and color distribution are calculated to generate saliency maps respectively, which are then fused using the principal component analysis. Compared with state-of-the-art models, the proposed model can accurately detect the salient object which conform with the human visual principle. The experimental results from the MSRA database validate the effectiveness of our proposed model.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61440016, 61375017, 61273225), the Natural Science Foundation of Hubei Provincial of China (2014CFB247), the open foundation of the key laboratory for metallurgical equipment and control of ministry of education (2013B08)

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Authors and Affiliations

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

    Xin Xu, Nan Mu, Li Chen & Xiaolong Zhang

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

    Xin Xu, Nan Mu, Li Chen & Xiaolong Zhang

Authors
  1. Xin Xu
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  2. Nan Mu
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  3. Li Chen
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  4. Xiaolong Zhang
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Correspondence to Xin Xu.

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Xu, X., Mu, N., Chen, L. et al. Hierarchical salient object detection model using contrast-based saliency and color spatial distribution. Multimed Tools Appl 75, 2667–2679 (2016). https://doi.org/10.1007/s11042-015-2570-0

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  • DOI: https://doi.org/10.1007/s11042-015-2570-0

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