  State-of-the-art computer vision approaches rely on huge amounts of annotated
data. The collection of such data is a time consuming process since it is
mainly performed by humans. The literature shows that semi-automatic annotation
approaches can significantly speed up the annotation process by the automatic
generation of annotation proposals to support the annotator. In this paper we
present a framework that allows for a quick and flexible design of
semi-automatic annotation pipelines. We show that a good design of the process
will speed up the collection of annotations. Our contribution is a new approach
to image annotation that allows for the combination of different annotation
tools and machine learning algorithms in one process. We further present
potential applications of our approach. The source code of our framework called
LOST (Label Objects and Save Time) is available at:
https://github.com/l3p-cv/lost.
