Label Assistant: A Workflow for Assisted Data Annotation in Image Segmentation Tasks

TL;DR

This paper introduces a workflow and software prototype to assist and streamline image annotation for segmentation tasks, aiming to reduce time, effort, and improve quality in data labeling.

Contribution

It proposes a generic, extendable workflow for assisted image annotation and demonstrates its implementation through a flexible software prototype.

Findings

Workflow improves annotation efficiency and quality

Software prototype supports various annotation steps

Potential to reduce annotation time and costs

Abstract

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.