Evaluating the Effect of Feedback from Different Computer Vision Processing Stages: A Comparative Lab Study
Authors
Computer vision and pattern recognition are increasingly being employed by smartphone and tablet applications targeted at lay-users. An open design challenge is to make such systems intelligible without requiring users to become technical experts. This paper reports a lab study examining the role of visual feedback. Our findings indicate that the stage of processing from which feedback is derived plays an important role in users' ability to develop coherent and correct understandings of a system's operation. Participants in our study showed a tendency to misunderstand the meaning being conveyed by the feedback, relating it to processing outcomes and higher level concepts, when in reality the feedback represented low level features. Drawing on the experimental results and the qualitative data collected, we discuss the challenges of designing interactions around pattern matching algorithms.
Research Questions / Practical Problems
Question signals indexed for this paper.
- 71%
Security Notifications in Static Analysis Tools: Developers' Attitudes, Comprehension, and Ability to Act on Them
CHI '21· Explainable AI (XAI) +2
- 71%
Designing Effective Training Dataset Explanations: The Impact of Information Depth and Progressive Disclosure
IUI '26· Explainable AI (XAI) +2
- 67%
Improving User Confidence in Concept Maps: Exploring Data Driven Explanations
CHI '18· Explainable AI (XAI) +1
- 63%
A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed Bandits
CHI '23· Explainable AI (XAI) +2
Based on Jaccard similarity of research subtopics & professions (≥60%)