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Learning salient seeds refer to the manifold ranking and background-prior strategy

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Abstract

Recently, the key technique of image processing has been widely applied to pattern recognition, content retrieval, and object segmentation. These applications have brought much higher complexity in image computation. Accordingly, the processed results may be interfered due to the interlacing reference. To overcome this problem, researchers have developed the object detection mechanism, which is a preprocessing procedure to extract significant feature to stand for the whole image. However, the error rate of detection is a crucial challenge in this research field. Based on the concept of manifold ranking, we have designed a brand-new object detection method considering both local and global features. The experimental results have demonstrated that the new method is able to lower down the detection error rate in case that the object located near the boundary.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Asia University, 500, Lioufeng Rd., Wufeng, Taichung, 41354, Taiwan

    Yung-Chen Chou

  2. Department of Information Engineering and Computer Science, Feng Chia University, 100 Wenhwa Rd., Seatwen, Taichung, 40724, Taiwan

    Yu-Wei Nien, Ying-Chin Chen & Jung-San Lee

  3. Department of Computer Science, University of Illinois, Urbana-Champaign, USA

    Bo Li

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  1. Yung-Chen Chou
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  2. Yu-Wei Nien
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  3. Ying-Chin Chen
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  4. Bo Li
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  5. Jung-San Lee
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Correspondence to Jung-San Lee.

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Chou, YC., Nien, YW., Chen, YC. et al. Learning salient seeds refer to the manifold ranking and background-prior strategy. Multimed Tools Appl 79, 5859–5879 (2020). https://doi.org/10.1007/s11042-019-08299-1

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  • DOI: https://doi.org/10.1007/s11042-019-08299-1

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