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Bag of contour fragments for improvement of object segmentation

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Abstract

Many state-of-the-art shape features have been proposed for the shape recognition task. In this paper, to explore whether a shape feature influences object segmentation, we propose a specific shape feature, Fisher shape (a form of bag of contour fragments), and we combine this with the appearance feature with multiple kernel learning to create a pipeline of object segmentation system. The experimental results on benchmark datasets clearly demonstrate that the pipeline of object segmentation is effective and that the Fisher shape can improve object segmentation with only the appearance feature.

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Acknowledgments

We thank the anonymous reviewers for their helpful suggestions. This work was supported by the scientific research fund of Jiangsu University of Technology (KYY17022), the Natural Science Fund Project of Colleges in Jiangsu Province (18KJB520013), Zhejiang Provincial the Natural Science Foundation of China (LQ19F020003), National Nature Science Foundation of China (Grant Nos. 61771146, 61806088, 61472166), the Natural Science Fund of Changzhou (CE20175026) and Qing Lan Project of Jiangsu Province.

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Authors and Affiliations

  1. School of Computer Engineering, Jiangsu University of Technology, Changzhou, People’s Republic of China

    Qian Yu, Honghui Fan, Hongjin Zhu & Feiyue Ye

  2. Laboratory of Cognitive Model and Algorithm, Department of Computer Science, Fudan University, Shanghai, People’s Republic of China

    Qian Yu & Hui Wei

  3. School of Information, Zhejiang University of Finance and Economics, Hangzhou, People’s Republic of China

    Chengzhuan Yang

  4. Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, People’s Republic of China

    Hui Wei

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  1. Qian Yu
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Yu, Q., Yang, C., Fan, H. et al. Bag of contour fragments for improvement of object segmentation. Appl Intell 50, 203–221 (2020). https://doi.org/10.1007/s10489-019-01525-1

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