Spatial Labeling: Leveraging Spatial Layout for Improving Label Quality in Non-Expert Image Annotation


Non-expert annotators (who lack sufficient domain knowledge) are often recruited for manual image labeling tasks owing to the lack of expert annotators. In such a case, label quality may be relatively low. We propose leveraging the spatial layout for improving label quality in non-expert image annotation. In the proposed system, an annotator first spatially lays out the incoming images and labels them on an open space, placing related items together. This serves as a working space (spatial organization) for tentative labeling. During the process, the annotator observes and organizes the similarities and differences between the items. Finally, the annotator provides definitive labels to the images based on the results of the spatial layout. We ran a user study comparing the proposed method and a traditional non-spatial layout in an image labeling task. The results demonstrated that annotators can complete the labeling tasks more accurately using the spatial layout interface than the non-spatial layout interface.

Chia-Ming Chang
The University of Tokyo, Tokyo, Japan
Chia-Hsien Lee
LeadBest Consulting Group, Taipei, Taiwan
Takeo Igarashi
The University of Tokyo, Tokyo, Japan




会議: CHI 2021

The ACM CHI Conference on Human Factors in Computing Systems (

セッション: Engineering Development Support

[A] Paper Room 05, 2021-05-10 17:00:00~2021-05-10 19:00:00 / [B] Paper Room 05, 2021-05-11 01:00:00~2021-05-11 03:00:00 / [C] Paper Room 05, 2021-05-11 09:00:00~2021-05-11 11:00:00
Paper Room 05
14 件の発表
2021-05-10 17:00:00
2021-05-10 19:00:00