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.
Video [2m13s]
Publication
Chia-Ming Chang, Yi He, Xi Yang, Haoran Xie, and Takeo Igarashi. 2022. DualLabel: Secondary Labels for Challenging Image Annotation. The 48th International Conference on Graphics Interface and Human-Computer Interaction (Gl 2022), Montreal, QC, Canada, 17-19 May 2022[PDF]
Related Publications
C. M. Chang, X. Yang, and T. Igarashi. 2022. An Empirical Study on the Effect of Quick and Careful Labeling Styles in Image Annotation. The 48th International Conference on Graphics Interface and Human-Computer Interaction (Gl 2022), Montreal, QC, Canada, 17-19 May 2022 [PDF]
C. M. Chang, C. H. Lee, and T. Igarashi. 2021. Spatial Labeling: Leveraging Spatial Layout for Improving Label Quality in Non-Expert Image Annotation. In CHI Conference on Human Factors in Computing Systems (CHI ’21), Yokohama, Japan. May 8–13, 2021 [PDF]
C. M. Chang, S. D. Mishra and T. Igarashi, 2019, A Hierarchical Task Assignment for Manual Image Labeling. The IEEE Symposium on Visual Languages & Human-Centric Computing (VL/HCC 2019), Memphis, Tennessee, US, 14-18 October 2019 [PDF]
