Advertisement

Set Similarity Measures for Images Based on Collective Knowledge

  • Valentina FranzoniEmail author
  • Clement H. C. Leung
  • Yuanxi Li
  • Paolo Mengoni
  • Alfredo Milani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9155)

Abstract

This work introduces a new class of group similarity where different measures are parameterized with respect to a basic similarity defined on the elements of the sets. Group similarity measures are of great interest for many application domains, since they can be used to evaluate similarity of objects in term of the similarity of the associated sets, for example in multimedia collaborative repositories where images, videos and other multimedia are annotated with meaningful tags whose semantics reflects the collective knowledge of a community of users. The group similarity classes are formally defined and their properties are described and discussed. Experimental results, obtained in the domain of images semantic similarity by using search engine based tag similarity, show the adequacy of the proposed approach in order to reflect the collective notion of semantic similarity.

Keywords

Group similarity Semantic distance Image retrieval Data mining Collective knowledge Knowledge discovery 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Miller, G.A.: Wordnet: a lexical database for english. Communications of the ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  2. 2.
    Budanitsky, A., Hirst, G.: Semantic distance in wordnet: an experimental, application-oriented evaluation of five measures. In: Proceedings of Workshop on WordNet and Other Lexical Resources, p. 641. North American Chapter of the Association for Computational Linguistics, Pittsburgh (2001)Google Scholar
  3. 3.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 448–453 (1995)Google Scholar
  4. 4.
    Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning, pp. 296–304. Morgan Kaufmann (1998)Google Scholar
  5. 5.
    Strube, M., Ponzetto, S.P.: WikiRelate! computing semantic relatedness using wikipedia. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence. AAAI Press, July 2006Google Scholar
  6. 6.
    Milne, D., Witten, I.H.: An effective, low-cost measure of semantic relatedness obtained from wikipedia links. In: Proceedings of first AAAI Workshop on Wikipedia and Artificial Intelligence, WIKIAI 2008, Chicago, IL, USA (2008)Google Scholar
  7. 7.
    Völkel, M., Krötzsch, M., Vrandecic, D., Haller, H., Studer, R.: Semantic wikipedia. In: Proceedings of the 15th International Conference on World Wide Web, WWW 2006, pp. 585–594. ACM, New York (2006)Google Scholar
  8. 8.
    Wu, L., Hua, X.-S., Yu, N., Ma, W.-Y., Li, S.: Flickr distance. In: Proceedings of the 16th ACM International Conference on Multimedia, MM 2008, New York, NY, USA, pp. 31–40 (2008)Google Scholar
  9. 9.
    Enser, P.G., Sandom, C.J., Lewis, P.H.: Surveying the reality of semantic image retrieval. In: Bres, S., Laurini, R. (eds.) VISUAL 2005. LNCS, vol. 3736, pp. 177–188. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Li, X., Chen, L., Zhang, L., Lin, F., Ma, W.: Image annotation by large-scale content-based image retrieval. In: Proceedings of the 14th Annual ACM International Conference on Multimedia, pp. 607–610 (2006)Google Scholar
  11. 11.
    Franzoni, V., Milani, A.: PMING Distance: A collaborative semantic proximity measure. In: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (IAT), vol. 2, pp. 442–449 (2012)Google Scholar
  12. 12.
    Leung, C.H., Li, Y., Milani, A., Franzoni, V.: Collective evolutionary concept distance based query expansion for effective web document retrieval. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.-Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2013, Part IV. LNCS, vol. 7974, pp. 657–672. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  13. 13.
    Manning, D., Schutze, H.: Foundations of statistical natural language processing. The MIT Press, London (2002)Google Scholar
  14. 14.
    Turney P.: Mining the web for synonyms: PMI versus LSA on TEOFL. In Proc. ECML (2001)Google Scholar
  15. 15.
    Chan, A.W.S., Liu, J., et al.: Intelligent Social Media Indexing and Sharing Using an Adaptive Indexing Search Engine. ACM TIST 3(3), 47 (2012). doi: 10.1145/2168752.2168761 MathSciNetGoogle Scholar
  16. 16.
    Li, Y.X.: Semantic Image Similarity Based on Deep Knowledge for Effective Image Retrieval. Research Thesis (2014)Google Scholar
  17. 17.
    Cheng, V.C., Liu, J., et al.: Probabilistic Aspect Mining Model for Drug Reviews. IEEE Transactions on Knowledge and Data Engineering 99, 1 (2014). doi: 10.1109/TKDE.2013.175. vol. 99, no. PrePrints, p. 1CrossRefGoogle Scholar
  18. 18.
    Franzoni, V., Milani, A.: Heuristic semantic walk for concept chaining in collaborative networks. International Journal of Web Information Systems 10(1), 85–103 (2014). doi: 10.1108/IJWIS-11-2013-0031 CrossRefGoogle Scholar
  19. 19.
    Franzoni, V., Mencacci, M., Mengoni, P., Milani, A.: Heuristics for semantic path search in Wikipedia. In: Murgante, B., et al. (eds.) ICCSA 2014, Part VI. LNCS, vol. 8584, pp. 327–340. Springer, Heidelberg (2014)Google Scholar
  20. 20.
    Franzoni, V., Milani, A.: Heuristic Semantic Walk. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.-Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2013, Part IV. LNCS, vol. 7974, pp. 643–656. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  21. 21.
    Franzoni V., Milani A.: Semantic Context Extraction from Collaborative Networks. In: IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD), Calabria, Italy (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Valentina Franzoni
    • 1
    • 2
    Email author
  • Clement H. C. Leung
    • 3
  • Yuanxi Li
    • 3
  • Paolo Mengoni
    • 1
  • Alfredo Milani
    • 1
    • 3
  1. 1.Department of Mathematics and Computer ScienceUniversity of PerugiaPerugiaItaly
  2. 2.Department of Computer, Control and Mgmt. EngineeringUniversity of Rome La SapienzaRomeItaly
  3. 3.Department of Computer ScienceHong Kong Baptist UniversityKowloon TongHong Kong

Personalised recommendations