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Published in:
2013 | OriginalPaper | Chapter
Exploiting Time in Automatic Image Tagging
Authors : Philip J. McParlane, Joemon M. Jose
Published in: Advances in Information Retrieval
Publisher: Springer Berlin Heidelberg
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Existing automatic image annotation (AIA) models that depend solely on low-level image features often produce poor results, particularly when annotating real-life collections. Tag co-occurrence has been shown to improve image annotation by identifying additional keywords associated with user-provided keywords. However, existing approaches have treated tag co-occurrence as a static measure over time, thereby ignoring the temporal trends of many tags. The temporal distribution of tags, however, caused by events, seasons, memes, etc. provide a strong source of evidence beyond keywords for AIA. In this paper we propose a temporal tag co-occurrence approach to improve upon the current state-of-the-art automatic image annotation model. By replacing the annotated tags with more temporally significant tags, we achieve statistically significant increases to annotation accuracy on a real-life timestamped image collection from Flickr.