Label Assistant: A Workflow for Assisted Data Annotation in Image Segmentation Tasks
This addresses the need for efficient data annotation in computer vision, but it is incremental as it reviews existing methods without introducing a new paradigm.
The paper tackles the problem of time-consuming and expensive human annotation for image segmentation by proposing a generic workflow to assist the annotation process, resulting in a flexible software prototype implementation.
Recent research in the field of computer vision strongly focuses on deep learning architectures to tackle image processing problems. Deep neural networks are often considered in complex image processing scenarios since traditional computer vision approaches are expensive to develop or reach their limits due to complex relations. However, a common criticism is the need for large annotated datasets to determine robust parameters. Annotating images by human experts is time-consuming, burdensome, and expensive. Thus, support is needed to simplify annotation, increase user efficiency, and annotation quality. In this paper, we propose a generic workflow to assist the annotation process and discuss methods on an abstract level. Thereby, we review the possibilities of focusing on promising samples, image pre-processing, pre-labeling, label inspection, or post-processing of annotations. In addition, we present an implementation of the proposal by means of a developed flexible and extendable software prototype nested in hybrid touchscreen/laptop device.