Abstract
How to establish the relationship between concepts based on the large scale real-world click data from commercial engine is a challenging topic due to that the click data suffers from the noise such as typos, the same concept with different queries etc.
In this paper, we propose an approach for automatically establishing the concept relationship. We first define five specific relationships between concepts and leverage them to annotate the images collected from commercial search engine. We then extract some conceptual features in textual and visual domain to train the concept model. The relationship of each pairwise concept will thus be classified into one of the five special relationships. Experimental results demonstrate our proposed approach is more effective than Google Distance.
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
Cilibrasi, R., Vitányi, P.M.B.: The google similarity distance. IEEE Trans. Knowl. Data Eng. 19(3), 370–383 (2007)
Cilibrasi, R., Vitányi, P.M.B.: Clustering by compression. CoRR, cs.CV/0312044 (2003)
Hua, X.-S., Yang, L., Wang, J., Wang, J., Ye, M., Wang, K., Rui, Y., Li, J.: Clickage: towards bridging semantic and intent gaps via mining click logs of search engines. In: ACM Multimedia, pp. 243–252 (2013)
Huang, H., Cheng, Y., Zhao, R.: A semi-supervised clustering algorithm based on must-link set. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds.) ADMA 2008. LNCS (LNAI), vol. 5139, pp. 492–499. Springer, Heidelberg (2008)
Lenat, D.B.: Cyc: A large-scale investment in knowledge infrastructure. Commun. ACM 38(11), 32–38 (1995)
Levina, E., Bickel, P.J.: The earth mover’s distance is the mallows distance: Some insights from statistics. In: ICCV, pp. 251–256 (2001)
Miller, G.A.: Wordnet, a lexical database for the english language, Cognition Science Lab. Princeton University (1995)
Wang, B., Li, Z., Li, M., Ma, W.-Y.: Large-scale duplicate detection for web image search. In: ICME, pp. 353–356 (2006)
Wu, L., Hua, X.-S., Yu, N., Ma, W.-Y., Li, S.: Flickr distance. In: ACM Multimedia, pp. 31–40 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Cao, W., Hong, R., Wang, M., Hua, X. (2014). Multifold Concept Relationships Metrics. In: Ooi, W.T., Snoek, C.G.M., Tan, H.K., Ho, CK., Huet, B., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2014. PCM 2014. Lecture Notes in Computer Science, vol 8879. Springer, Cham. https://doi.org/10.1007/978-3-319-13168-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-13168-9_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13167-2
Online ISBN: 978-3-319-13168-9
eBook Packages: Computer ScienceComputer Science (R0)