Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Learning facial attributes by crowdsourcing in social media
Proceedings of the 20th international conference companion on World wide web
Do you need experts in the crowd?: a case study in image annotation for marine biology
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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Obtaining large quantities of labeled data of sufficient quality is non-trivial, especially when expert knowledge is required. Experts are scarce and expensive, while laymen lack the necessary knowledge to perform the task. In this demo paper, we present an image labeling tool Fish4label. By carefully converting an object recognition task to a visual similarity comparison task, our tool enables laymen to identify fish species in images extracted from video footage taken by underwater cameras, a task that typically requires profound domain knowledge in marine biology.