Journal
32ND ANNUAL ACM CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI 2014)
Volume -, Issue -, Pages 3099-3102Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2556288.2557011
Keywords
Human computation; Crowdsourcing
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We study strategies for scalable multi-label annotation, or for efficiently acquiring multiple labels from humans for a collection of items. We propose an algorithm that exploits correlation, hierarchy, and sparsity of the label distribution. A case study of labeling 200 objects using 20,000 images demonstrates the effectiveness of our approach. The algorithm results in up to 6x reduction in human computation time compared to the naive method of querying a human annotator for the presence of every object in every image.
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