DualLabel: Secondary Labels for Challenging Image Annotation

Chia-Ming Chang, Yi He, Xi Yang, Haoran Xie, Takeo Igarashi

Keywords: Annotation Tool, Challenging Image Annotation, Non-Expert Annotator, Secondary Label, Machine Learning

TL;DR: DualLabel, an annotation tool that allows annotators to assign secondary labels for challenging image annotation

Abstract: Non-expert annotators must select an appropriate label for an image when the annotation task is difficult. Then, it might be easier for an annotator to choose multiple “likely” labels than to select a single label. Multiple labels might be more informative in the training of a classifier because multiple labels can have the correct one, even when a single label is incorrect. We present DualLabel, an annotation tool that allows annotators to assign secondary labels to an image to simplify the annotation process and improve the classification accuracy of a trained model. A user study compared the proposed dual-label and traditional single- label approaches for an image annotation task. The results show that our dual-label approach reduces task completion time and improves classifier accuracy trained with the given labels.