Abstract
Recommending text tags for on-line photos is useful for on-line photo services. We propose a novel approach to tag recommendation by utilizing both the underlying semantic correlation between visual contents and text tags and the tag popularity learnt from realistic on-line photos. We apply our approach to a database of real on-line photos and evaluate its performance by both objective and subjective evaluation. Experiwith ments demonstrate the improved performance of the proposed approach compared the state-of-the-art techniques in the literature.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Y. Mori, H. Takahashi, and R. Oka, “Image-to-word transformation based on dividing and vector quantizing images with words,” presented at International Workshop on Multimedia Intelligent Storage and Retrieval Management, 1999.
T. Kolenda, L. K. Hansen, J. Larsen, and O. Winther, “Independent component analysis for understanding multimedia content,” presented at IEEE Workshop on Neural Networks for Signal Processing XII, 2002.
K. Barnard, P. Duygulu, N. d. Freitas, D. Forsyth, D. Blei, and M. I. Jordan, “Matching Words and Pictures,” Journal of Machine Learning Research, vol. 3, pp. 1107–1135, 2003.
J. Li and J. Z. Wang, “Real-Time Computerized Annotation of Pictures,” presented at ACM MM, Santa Barbara, USA, 2006.
P. Duygulu, K. Barnard, N. d. Fretias, and D. Forsyth, “Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary,” presented at European Conference on Computer Vision (ECCV), 2002.
B. Sigurbjornsson and R. v. Zwol, “Flickr Tag Recommendation based on Collective Knowledge,” presented at ACM WWW2008, Beijing, China, 2008.
P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: An open architecture for collaborative filtering of netnews,” presented at ACM Conference on Computer Supported Cooperative Work, Chapel Hill, NC, 1994.
N. D.M., “Implicit Rating and Filtering,” presented at Fifth DELOS Workshop on Filtering and Collaborative Filtering, Budapest, Hungary., 1997.
C. W.-k. Leung, S. C.-f. Chan, and F.-l. Chung, “A collaborative filtering framework based on fuzzy association rules and multiple-level similarity,” Knowledge and Information Systems, vol. 10, pp. 357–381, 2006.
S. Deerwester, S. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, “Indexing by latent semantic analysis,” Journal of the Society for Information Science., vol. 41, pp. 391–407, 1990.
T. K. Landauer, D. S. McNamara, S. Dennis, and W. Kintsch, Handbook of Latent Semantic Analysis: Psychology Press, 2007.
T. K. Landauer, P. W. Foltz, and D. Laham, “Introduction to Latent Semantic Analysis,” Discourse Processes, vol. 25, pp. 259–284, 1998.
D. R. Hardoon, S. R. Szedmak, and J. R. Shawe-taylor, “Canonical correlation analysis: An overview with application to learning methods,” Neural Computation, vol. 16, pp. 2639–2664, 2004.
H. Hotelling, “Relations between two sets of variates,” Biometrika, vol. 28, pp. 312–377, 1936.
A. Vinokourov, J. Shawe-Taylor, and N. Cristianini, “Inferring a semantic representation of text via cross-language correlation analysis,” presented at NIPS, 2002.
S. Subramanya, Z. Wang, B. Li, and H. Liu, “Completing Missing Views for Multiple Sources of Web Media,” International Journal of Data Mining, Modelling and Managment (IJDMMM), vol. 1.
N. Agarwal, H. Liu, and J. Zhang, “Blocking objectionable web content by leveraging multiple information sources,” SIGKDD Explor. Newsl., vol. 8, pp. 17–26, 2006.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2009 Springer-Verlag US
About this paper
Cite this paper
Wang, Z., Li, B. (2009). Learning to Recommend Tags for On-line Photos. In: Social Computing and Behavioral Modeling. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0056-2_29
Download citation
DOI: https://doi.org/10.1007/978-1-4419-0056-2_29
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-0055-5
Online ISBN: 978-1-4419-0056-2
eBook Packages: Computer ScienceComputer Science (R0)