Interactive Part Segmentation Using Edge Images

Title
Interactive Part Segmentation Using Edge Images
Authors
박정민오주영
Keywords
Interactive segmentation; part segmentation; object segmentation
Issue Date
2021-10
Publisher
Applied Sciences-basel
Citation
VOL 11, NO 21, 10106
Abstract
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.
URI
http://pubs.kist.re.kr/handle/201004/74088
ISSN
2076-3417
Appears in Collections:
KIST Publication > Article
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