Speed Labeling: Non-stop Scrolling for Fast Image Labeling

Chia-Ming Chang, Yi Tang, Xi Yang, Xiang Chen, Takeo Igarashi

Abstract: This study presents “Speed Labeling”, an image-labeling technique to increase the efficiency of easy binary labeling tasks where an annotator can choose a label instantly. We first conduct a formative study to identify the factors affecting the efficiency of easy image labeling: image layout and image transition. Based on these results, we designed a novel labeling technique using non- stop scrolling. In conventional image labeling, the system moves to the next image only after the user assigns a label to the previous image. To maximize efficiency, our technique continuously scrolls images without waiting for the completion of labeling, assuming that the user gives labels at a mostly constant speed. The system dynamically adjusts the scrolling speed based on the labeling speed. Subsequently, we conduct a user study to compare the proposed “non-stop scrolling” technique to the conventional “stop-and-go scrolling” technique in an easy image-labeling task. The results showed that speed labeling requires less time (faster by 7%, 305 more images labeled per man-hour) to complete the labeling task than the conventional technique without a significant increase in errors. In addition, the results showed that speed labeling makes the labeling task more enjoyable for crowd workers and makes them feel more attentive during tasks.

External IDs:dblp:conf/graphicsinterface/0003T0CI24