Active instance segmentation with fractional-order network and reinforcement learning

Xueming Li, Guohao Wu, Shangbo Zhou, Xiaoran Lin, Xu Li

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.