.png){kind=icon}
.png){kind=icon}
Return
Rethinking interactive image segmentation: Feature space annotation
.png){kind=icon}
OA
AI
DOI：10.1016/j.patcog.2022.108882.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Despite the progress of interactive image segmentation methods, high-quality pixel-level annotation is still time-consuming and laborious - a bottleneck for several deep learning applications. We take a step back to propose interactive and simultaneous segment annotation from multiple images guided by feature space projection. This strategy is in stark contrast to existing interactive segmentation methodologies, which perform annotation in the image domain. We show that feature space annotation achieves com-petitive results with state-of-the-art methods in foreground segmentation datasets: iCoSeg, DAVIS, and Rooftop. Moreover, in the semantic segmentation context, it achieves 91.5% accuracy in the Cityscapes dataset, being 74.75 times faster than the original annotation procedure. Further, our contribution sheds light on a novel direction for interactive image annotation that can be integrated with existing method-ologies. The supplementary material presents video demonstrations. Code available at https://github.com/ LIDS- UNICAMP/rethinking- interactive- image-segmentation . (c) 2022 Elsevier Ltd. All rights reserved.
Keywords:
Interactive image segmentation
Data annotation
Interactive machine learning
Feature space annotation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W