ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (8): 1705-1717.doi: 10.7544/issn1000-1239.2021.20210195

Special Issue: 2021人工智能前沿进展专题

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Deep Interactive Image Segmentation Based on Fusion Multi-Scale Annotation Information

Ding Zongyuan1, Sun Quansen1, Wang Tao1, Wang Hongyuan2   

  1. 1(School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094);2(School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu 213164)
  • Online:2021-08-01
  • Supported by: 
    This work was supported by the National Natual Science Foundation of China (61802188, 61673220, 61976028), the Natural Science Foundation of Jiangsu Province (BK20180458), and the China Postdoctoral Science Foundation (2020M681530).

Abstract: Existing deep interactive image segmentation algorithms calculate distance maps or Gaussian maps for the click-annotations, and then concatenate them with the image as the input of network. The influence range of each click is the same, but the purpose of each interaction is different. The main role of the early interaction is selection, and the later prefers fine-tuning. To this end, a deep interactive image segmentation algorithm fused with multi-scale label information is proposed. First, by setting different Gaussian radius, two groups of Gaussian maps with different scales are calculated for each click. Secondly, by fusing with the small scale Gaussian maps, and some down-sampling modules in the basic segmentation network are removed, hence richer detailed features of targets are extracted. At the same time, in order to maintain the integrity of the target segmentation results, a non-local feature attention module is proposed and this module fuses large scale Gaussian maps. Finally, according to the probability information provided by the Gaussian map, a probability click loss is proposed to enhance the segmentation performance of the target near the click point. Experimental results show that the proposed algorithm can not only maintain the integrity of the segmentation, but also obtain the segmentation results of the target details, which greatly reduces the user’s interaction burden.

Key words: interactive image segmentation, deep learning, multi-scale annotation, Gaussian maps, probability click loss

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