Applied Sciences (Oct 2021)

Interactive Part Segmentation Using Edge Images

  • Ju-Young Oh,
  • Jung-Min Park

DOI
https://doi.org/10.3390/app112110106
Journal volume & issue
Vol. 11, no. 21
p. 10106

Abstract

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As more and more fields utilize deep learning, there is an increasing demand to make suitable training data for each field. The existing interactive object segmentation models can easily make the mask label data because these can accurately segment the area of the target object through user interaction. However, it is difficult to accurately segment the target part in the object using the existing models. We propose a method to increase the accuracy of part segmentation by using the proposed interactive object segmentation model trained only with edge images instead of color images. The results evaluated with the PASCAL VOC Part dataset show that the proposed method can accurately segment the target part compared to the existing interactive object segmentation model and the semantic part-segmentation model.

Keywords