MMM 2011: Advances in Multimedia Modeling pp 140-150 | Cite as
Content-Based Multimedia Retrieval in the Presence of Unknown User Preferences
Abstract
Content-based multimedia retrieval requires an appropriate similarity model which reflects user preferences. When these preferences are unknown or when the structure of the data collection is unclear, retrieving the most preferable objects the user has in mind is challenging, as the notion of similarity varies from data to data, from task to task, and ultimately from user to user. Based on a specific query object and unknown user preferences, retrieving the most similar objects according to some default similarity model does not necessarily include the most preferable ones. In this work, we address the problem of content-based multimedia retrieval in the presence of unknown user preferences. Our idea consists in performing content-based retrieval by considering all possibilities in a family of similarity models simultaneously. To this end, we propose a novel content-based retrieval approach which aims at retrieving all potentially preferable data objects with respect to any preference setting in order to meet individual user requirements as much as possible. We demonstrate that our approach improves the retrieval performance regarding unknown user preferences by more than 57% compared to the conventional retrieval approach.
Keywords
content-based retrieval user preferences multimedia databasesPreview
Unable to display preview. Download preview PDF.
References
- 1.Beecks, C., Uysal, M.S., Seidl, T.: A comparative study of similarity measures for content-based multimedia retrieval. In: Proceedings of the IEEE International Conference on Multimedia & Expo., pp. 1552–1557 (2010)Google Scholar
- 2.Beecks, C., Uysal, M.S., Seidl, T.: Signature quadratic form distance. In: Proceedings of the ACM International Conference on Image and Video Retrieval, pp. 438–445 (2010)Google Scholar
- 3.Börzsönyi, S., Kossmann, D., Stocker, K.: The Skyline operator. In: Proceedings of the IEEE International Conference on Data Engineering, pp. 421–430 (2001)Google Scholar
- 4.Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image Retrieval: Ideas, Influences, and Trends of the New Age. ACM Computing Surveys 40(2), 1–60 (2008)CrossRefGoogle Scholar
- 5.Deselaers, T., Keysers, D., Ney, H.: Features for Image Retrieval: An Experimental Comparison. Information Retrieval 11(2), 77–107 (2008)CrossRefGoogle Scholar
- 6.Faloutsos, C., Barber, R., Flickner, M., Hafner, J., Niblack, W., Petkovic, D., Equitz, W.: Efficient and Effective Querying by Image Content. Journal of Intelligent Information Systems 3(3/4), 231–262 (1994)CrossRefGoogle Scholar
- 7.Ferreira, C., Santos, J., da, S., Torres, R., Gonc̨alves, M., Rezende, R., Fan, W.: Relevance feedback based on genetic programming for image retrieval. Pattern Recognition Letters (in Press, 2010)Google Scholar
- 8.Geetha, P., Narayanan, V.: A Survey of Content- Based Video Retrieval. Journal of Computer Science 4(6), 474–486 (2008)CrossRefGoogle Scholar
- 9.Heesch, D.: A survey of browsing models for content based image retrieval. Multimedia Tools and Applications 40(2), 261–284 (2008)CrossRefGoogle Scholar
- 10.Hu, R., Rüger, S.M., Song, D., Liu, H., Huang, Z.: Dissimilarity measures for content-based image retrieval. In: Proceedings of the IEEE International Conference on Multimedia & Expo., pp. 1365–1368 (2008)Google Scholar
- 11.Ishikawa, Y., Subramanya, R., Faloutsos, C.: Mindreader: Querying databases through multiple examples. In: Proceedings of the International Conference on Very Large Data Bases, pp. 218–227 (1998)Google Scholar
- 12.Katzner, D.W.: An introduction to the economic theory of market behavior: microeconomics from a Walrasian perspective. Edward Elgar Publishing (2008)Google Scholar
- 13.Kung, H.T., Luccio, F., Preparata, F.P.: On finding the maxima of a set of vectors. Journal of the ACM 22(4), 469–476 (1975)MathSciNetCrossRefMATHGoogle Scholar
- 14.Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-Based Multimedia Information Retrieval: State of the Art and Challenges. ACM Transactions on Multimedia Computing, Communications and Applications 2(1), 1–19 (2006)CrossRefGoogle Scholar
- 15.Papadias, D., Tao, Y., Fu, G., Seeger, B.: An optimal and progressive algorithm for skyline queries. In: Proceedings of the ACM International Conference on Management of Data, pp. 467–478 (2003)Google Scholar
- 16.Preparata, F.P., Shamos, M.I.: Computational Geometry: An Introduction. Springer, Heidelberg (1985)CrossRefMATHGoogle Scholar
- 17.Sacharidis, D., Bouros, P., Sellis, T.: Caching dynamic skyline queries. In: Ludäscher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol. 5069, pp. 455–472. Springer, Heidelberg (2008)CrossRefGoogle Scholar
- 18.Sebe, N., Lew, M.S., Zhou, X., Huang, T.S., Bakker, E.M.: The state of the art in image and video retrieval. In: Proceedings of the 2nd International Conference on Image and Video Retrieval, pp. 7–12 (2003)Google Scholar
- 19.Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
- 20.Tan, K.L., Eng, P.K., Ooi, B.C.: Efficient progressive skyline computation. In: Proceedings of the International Conference on Very Large Data Bases, pp. 301–310 (2001)Google Scholar
- 21.Wang, J., Li, J., Wiederhold, G.: SIMPLIcity: semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(9), 947–963 (2001)CrossRefGoogle Scholar
- 22.Wichterich, M., Beecks, C., Sundermeyer, M., Seidl, T.: Exploring multimedia databases via optimization-based relevance feedback and the earth mover’s distance. In: Proceedings of the ACM Conference on Information and Knowledge Management, pp. 1621–1624 (2009)Google Scholar
- 23.Zhou, X., Huang, T.: Relevance feedback in image retrieval: A comprehensive review. Multimedia systems 8(6), 536–544 (2003)CrossRefGoogle Scholar