Understanding Human-side Impact of Sampling Image Batches in Subjective Attribute Labeling

Abstract

Capturing human annotators' subjective responses in image annotation has become crucial as vision-based classifiers expand the range of application areas. While there has been significant progress in image annotation interface design in general, relatively little research has been conducted to understand how to elicit reliable and cost-efficient human annotation when the nature of the task includes a certain level of subjectivity. To bridge this gap, we aim to understand how different sampling methods in image batch labeling, a design that allows human annotators to label a batch of images simultaneously, can impact human annotation performances. In particular, we developed three different strategies in forming image batches: (1) uncertainty-based labeling (UL) that prioritizes images that a classifier predicts with the highest uncertainty, (2) certainty-based labeling (CL), a reverse strategy of UL, and (3) random, a baseline approach that randomly selects images. Although UL and CL solely select images to be labeled from a classifier's point of view, we hypothesized that human-side perception and labeling performance may also vary depending on the different sampling strategies. In our study, we observed that participants were able to recognize a different level of perceived cognitive load across three conditions (CL the easiest while UL the most difficult). We also observed a trade-off between annotation task effectiveness (CL and UL more reliable than random) and task efficiency (UL the most efficient while CL the least efficient). Based on the results, we discuss the implications of design and possible future research directions of image batch labeling. © 2021 ACM.