McParlane, P. J. and Jose, J. M. (2013) Exploiting time in automatic image tagging. Lecture Notes in Computer Science, 7814, pp. 520-531. (doi: 10.1007/978-3-642-36973-5_44)
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Abstract
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.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Jose, Professor Joemon |
| Authors: | McParlane, P. J., and Jose, J. M. |
| College/School: | College of Science and Engineering > School of Computing Science |
| Journal Name: | Lecture Notes in Computer Science |
| Publisher: | Springer |
| ISSN: | 0302-9743 |
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