Rethinking Interactive Image Segmentation: Feature Space Annotation

Jordão Bragantini (1) , Alexandre Falcão (1) , Laurent Najman (2)

1 IC - Institute of Computing [Campinas]
2 LIGM - Laboratoire d'Informatique Gaspard-Monge

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 and optimized by metric learning as the labeling progresses. This strategy is in stark contrast to existing interactive segmentation methodologies, which perform annotation in the image domain. We show that our approach can surpass the accuracy of state-of-the-art methods in foreground segmentation datasets: iCoSeg, DAVIS, and Rooftop. Moreover, it achieves 91.5\% accuracy in a known semantic segmentation dataset, Cityscapes, being 74.75 times faster than the original annotation procedure. The appendix presents additional qualitative results. Code and video demonstration will be released upon publication.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Jordão Bragantini, Alexandre Falcão, Laurent Najman. Rethinking Interactive Image Segmentation: Feature Space Annotation. 2021. ⟨hal-03105751v1⟩