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Salient object detection via images frequency domain analyzing

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Abstract

Salient object detection has become an important direction in image processing and computer vision. The traditional center-priori theory believes that salient target should be closer to the central area of the image. However, false detection will often occur when the salient object is closer to the image boundary. So, this paper obtains center coordinates of the salient object by using Harris corner detection algorithm and convex hull. Accordingly, an improved center-priori saliency detection model is obtained by applying the frequency-tuned method. And then, the local saliency is set up by wavelet transforming which has the local characteristic information representation ability in the time domain and frequency domain. In addition, we obtain the global saliency by spectral residual analyzing. Finally, an advanced center-priori saliency model is established. The experimental results show that the model in this paper has better detection effects and higher target detection rates.

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Acknowledgments

This work has been supported by National Natural Science Foundation of China (61203261), China Postdoctoral Science Foundation Funded Project (2012M521335), Ministry of Education Key Laboratory of Intelligent Computing and Signal Processing (201201), open funding programme of Joint Laboratory of Flight Vehicle Ocean-based Measurement and Control (FOM2014OF004), Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology, Grant No. 30920140122007) and The Fundamental Research Funds of Shandong University (2014JC017).

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Authors and Affiliations

  1. School of Control Science and Engineering, Shandong University, Jinan, 250061, China

    Chao He, Zhenxue Chen & Chengyun Liu

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  1. Chao He
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  2. Zhenxue Chen
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  3. Chengyun Liu
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Correspondence to Zhenxue Chen.

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He, C., Chen, Z. & Liu, C. Salient object detection via images frequency domain analyzing. SIViP 10, 1295–1302 (2016). https://doi.org/10.1007/s11760-016-0954-x

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  • DOI: https://doi.org/10.1007/s11760-016-0954-x

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