Fast soft color segmentation

Naofumi Akimoto, Huachun Zhu, Yanghua Jin, Yoshimitsu Aoki

研究成果: Conference article査読

1 被引用数 (Scopus)

抄録

We address the problem of soft color segmentation, defined as decomposing a given image into several RGBA layers, each containing only homogeneous color regions. The resulting layers from decomposition pave the way for applications that benefit from layer-based editing, such as recoloring and compositing of images and videos. The current state-of-the-art approach for this problem is hindered by slow processing time due to its iterative nature, and consequently does not scale to certain real-world scenarios. To address this issue, we propose a neural network based method for this task that decomposes a given image into multiple layers in a single forward pass. Furthermore, our method separately decomposes the color layers and the alpha channel layers. By leveraging a novel training objective, our method achieves proper assignment of colors amongst layers. As a consequence, our method achieve promising quality without existing issue of inference speed for iterative approaches. Our thorough experimental analysis shows that our method produces qualitative and quantitative results comparable to previous methods while achieving a 300,000x speed improvement. Finally, we utilize our proposed method on several applications, and demonstrate its speed advantage, especially in video editing.

本文言語English
論文番号9156635
ページ(範囲)8274-8283
ページ数10
ジャーナルProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOI
出版ステータスPublished - 2020
イベント2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States
継続期間: 2020 6 142020 6 19

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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