Rethinking interactive image segmentation: Feature space annotation. (November 2022) - Bragantini, Jordão; Falcão, Alexandre X.; Najman, Laurent
Highlights: A method to fastly annotate various objects in multiple images at the same time. A new user interface paradigm for image annotation. A strategy to learn and improve feature space projection during data annotation. A method that avoids annotation of redundant image components. A fundamental step towards more efficient and effective active learning. Abstract: 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 competitive 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 methodologies. The supplementary material presents video demonstrations. Code available at https://github.com/LIDS-UNICAMP/rethinking-interactive-image-segmentation .