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Active instance segmentation with fractional-order network and reinforcement learning

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Abstract

In this paper, a novel model is proposed to segment image instance based on fractional-order chaotic synchronization network and reinforcement learning method. In the proposed model, fractional-order network is used for the preliminary image segmentation, which can obtain fine-grained information to provide a guiding strategy for the exploration of reinforcement learning; afterward, reinforcement learning method is committed to generate high-quality bounding contour curves for the object instances, which can combine the pixel features with local information in the image to improve the overall accuracy. Compared with other fractional-order models, the experimental results show that our proposed model achieves higher accuracy on the datasets of Pascal VOC2007 and Pascal VOC2012.

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Authors and Affiliations

  1. School of Computer, Chongqing University, Chongqing, 400000, China

    Xueming Li, Guohao Wu, Shangbo Zhou & Xu Li

  2. School of Information and Technology, Hebei University of Economics and Business, Hebei, 050000, China

    Xiaoran Lin

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  1. Xueming Li
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  2. Guohao Wu
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  3. Shangbo Zhou
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  4. Xiaoran Lin
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  5. Xu Li
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Correspondence to Xueming Li.

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Li, X., Wu, G., Zhou, S. et al. Active instance segmentation with fractional-order network and reinforcement learning. Vis Comput 38, 3027–3040 (2022). https://doi.org/10.1007/s00371-021-02174-7

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