Abstract
Digital Libraries contain collections of multimedia objects providing services for the management, sharing and retrieval. Involved objects have two levels of complexity: the former refers to the inner object complexity while the latter takes into account the implicit/explicit relationships among objects. Traditional machine learning classifiers do not consider the relationships among objects assuming them independent and identically distributed. Recently, link-based classification methods have been proposed, that try to classify objects exploiting their relationships (links). In this paper, we deal with objects corresponding to digital images, even if the proposed approach can be naturally applied to different kind of multimedia objects. Relationships can be expressed among the features of the same image or among features belonging to different images. The aim of this work is to verify whether a link-based classifier based on a Statistical Relational Learning (SRL) language can improve the accuracy of a classical k-nearest neighbour approach. Experiments will show that the modelling of the relationships in a real-word dataset using a SRL model reduces the classification error.
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References
Candela, L., Castelli, D., Ferro, N., Ioannidis, Y., Koutrika, G., Meghini, C., Pagano, P., Ross, S., Soergel, D., Agosti, M., Dobreva, M., Katifori, V., Schuldt, H.: The DELOS Digital Library Reference Model. Foundations for Digital Libraries (2007)
Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Getoor, L., Taskar, B.: Introduction to Statistical Relational Learning. The MIT Press (2007)
Getoor, L., Diehl, C.P.: Link mining: a survey. SIGKDD Explorations Newsletter 7, 3–12 (2005)
Getoor, L.: Link-based Classification. In: Maulik, U., Holder, L., Cook, D. (eds.) Advanced Methods for Knowledge Discovery from Complex Data. Springer, Heidelberg (2005)
Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J., Zabih, R.: Image indexing using color correlograms. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 762–768 (1997)
Raedt, L.D., Kimmig, A., Toivonen, H.: Problog: a probabilistic prolog and its application in link discovery. In: Proceedings of 20th International Joint Conference on Artificial Intelligence, pp. 2468–2473. AAAI Press (2007)
Nowak, E., Jurie, F., Triggs, B.: Sampling Strategies for Bag-of-Features Image Classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 490–503. Springer, Heidelberg (2006)
Hofmann, T.: Probabilistic latent semantic analysis. In: Proceeding of the Fifteenth Conference on Uncertainty in Artificial Intelligence (1995)
Blei, D.M., Ng, A.Y., Jordan, M.I., Lafferty, J.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Communication ACM 18, 613–620 (1975)
Burl, M.C., Weber, M., Perona, P.: A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998, Part II. LNCS, vol. 1407, pp. 628–641. Springer, Heidelberg (1998)
Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 264–271 (2003)
Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. International Journal of Pattern Recognition and Artificial Intelligence, 265–298 (2004)
Sen, P., Namata, G.M., Bilgic, M., Getoor, L., Gallagher, B., Eliassi-Rad, T.: Collective classification in network data. AI Magazine 29, 93–106 (2008)
Macskassy, S.A., Provost, F.: A simple relational classifier. In: Proceedings of the Second Workshop on Multi-Relational Data Mining, pp. 64–76 (2003)
Macskassy, S.A., Provost, F.: Classification in networked data: A toolkit and a univariate case study. Journal of Machine Learning Research 8, 935–983 (2007)
Chakrabarti, S., Dom, B., Indyk, P.: Enhanced hypertext categorization using hyperlinks. In: Proceedings SIGMOD International Conference on Management of Data, pp. 307–318. ACM (1998)
Lu, Q., Getoor, L.: Link-based classification. In: Proceedings of the International Conference on Machine Learning, pp. 496–503 (2003)
Taranto, C., Di Mauro, N., Ferilli, S., Esposito, F.: Approximate image color correlograms. In: Proceedings of the International Conference on Multimedia, MM 2010, pp. 1127–1130. ACM (2010)
Poole, D.: Abducing through negation as failure: stable models within the independent choice logic. J. Log. Program. 44, 5–35 (2000)
Kameya, Y., Ueda, N., Sato, T.: A Graphical Method for Parameter Learning of Symbolic-Statistical Models. In: Arikawa, S., Nakata, I. (eds.) DS 1999. LNCS (LNAI), vol. 1721, pp. 264–276. Springer, Heidelberg (1999)
Costa, V.S., Page, D., Qazi, M., Cussens, J.: CLP(BN): Constraint logic programming for probabilistic knowledge. In: Proceedings of the Nineteenth International Conference on Uncertainty in AI, pp. 517–524 (2003)
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. The MIT Press (2009)
Sato, T.: A statistical learning method for logic programs with distribution semantics. In: Proceedings of the 12th International Confernce on Logic Programming, pp. 715–729. MIT Press (1995)
Kimmig, A., Santos Costa, V., Rocha, R., Demoen, B., De Raedt, L.: On the Efficient Execution of ProbLog Programs. In: Garcia de la Banda, M., Pontelli, E. (eds.) ICLP 2008. LNCS, vol. 5366, pp. 175–189. Springer, Heidelberg (2008)
Shterionov, D., Kimmig, A., Mantadelis, T., Janssens, G.: DNF sampling for problog inference. In: International Colloquium on Implementation of Constraint and Logic Programming Systems (2010)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In: IEEE Computer Vision and Pattern Recognition, Workshop on Generative-Model Based Vision (2004)
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Taranto, C., Di Mauro, N., Esposito, F. (2011). Probabilistic Inference over Image Networks. In: Agosti, M., Esposito, F., Meghini, C., Orio, N. (eds) Digital Libraries and Archives. IRCDL 2011. Communications in Computer and Information Science, vol 249. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27302-5_1
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DOI: https://doi.org/10.1007/978-3-642-27302-5_1
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