Abstract
Content based image retrieval systems provide techniques for representing, indexing and searching images. They address only the user’s short term needs expressed as queries. From the importance of the visual information in many applications such as advertisements and security, we motivate in this paper, the Content Based Image Suggestion. It targets the user’s long term needs as a recommendation of products based on the user preferences in different situations, and on the visual content of images. We propose a generative model in which the visual features and users are clustered into separate classes. We identify the number of both user and image classes with the simultaneous selection of relevant visual features. The goal is to ensure an accurate prediction of ratings for multidimensional images. This model is learned using the minimum message length approach. Experiments with an image collection showed the merits of our approach.
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References
Belk, R.W.: Situational Variables and Consumer Behavior. Journal of Consumer Research 2, 157–164 (1975)
Bouguila, N., Ziou, D.: A Hybrid SEM Algorithm for High-Dimensional Unsupervised Learning Using a Finite Generalized Dirichlet Mixture. IEEE Trans. on Image Processing 15(9), 1785–1803 (2006)
Bouguila, N., Ziou, D.: High-dimensional unsupervised selection and estimation of a finite generalized dirichlet mixture model based on minimum message length. IEEE Trans. on PAMI (2007)
Boutemedjet, S., Ziou, D.: Content-based collaborative filtering model for scalable visual document recommendation. In: Proc. of IJCAI-2007 Workshop on Multimodal Information Retrieval (2007)
Connor, R.J., Mosimann, J.E.: Concepts of Independence for Proportions With a Generalization of the Dirichlet Distribution. Journal of the American Statistical Association 39, 1–38 (1977)
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, Springer, Heidelberg (2004)
Dy, J.G., Brodley, C.E.: Feature selection for unsupervised learning. Journal of Machine Learning Research 5, 845–889 (2004)
Figueiredo, M.A.T., Jain, A.K.: Unsupervised learning of finite mixture models. IEEE Trans. on PAMI 24(3), 4–37 (2002)
Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Hofmann, T.: Probabilistic Latent Semantic Indexing. In: Proc. of SIGIR (1999)
Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J., Zabih, R.: Image indexing using color correlograms. In: Proc. of IEEE Conf., IEEE Computer Society Press, Los Alamitos (1997)
Jain, A., Vailaya, A.: Image retrieval using color and shape. Pattern Recognition 29(8), 1233–1244 (1996)
Kontkanen, P., Myllymki, P., Silander, T., Tirri, H., Grnwald, P.: On predictive distributions and bayesian networks. Statistics and Computing 10(1), 39–54 (2000)
Law, M.H.C., Figueiredo, M.A.T., Jain, A.K.: Simultaneous feature selection and clustering using mixture models. IEEE Trans. on PAMI, 26(9) (2004)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Muramastsu, J., Pazzani, M., Billsus, D.: Syskill and Webert: Identifying Interesting Web Sites. In: Proc. of AAAI (1996)
Marlin, B.: Modeling User Rating Profiles For Collaborative Filtering. In: Proc. of NIPS (2003)
Messaris, P.: Visual Persuasion: The Role of Images in Advertising. Sage Pubns, Thousand Oaks (1997)
Mooney, R.J., Roy, L.: Content-Based Book Recommending Using Learning for Text Categorization. In: Proc. 5th ACM Conf. Digital Libaries, ACM Press, New York (2000)
Ng, A.Y.: On feature selection: Learning with exponentially many irrelevant features as training examples. In: Proc. of ICML (1998)
Novovicova, J., Pudil, P., Kittler, J.: Divergence based feature selection for multimodal class densities. IEEE Trans. on PAMI 18(2), 218–223 (1996)
Popescul, A., Ungar, L.H., Pennock, D.M., Lawrence, S.: Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments. In: Proc. of UAI (2001)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An Open Architecture for Collaborative Filtering of Netnews. In: Proc. of ACM Conference on CSCW, ACM Press, New York (1994)
Si, L., Jin, R.: Flexible Mixture Model for Collaborative Filtering. In: Proc. of ICML, pp. 704–711 (2003)
Ueda, N., Nakano, R.: Deterministic Annealing EM Algorithm. Neural Networks 11(2), 271–282 (1998)
Vaithyanathan, S., Dom, B.: Generalized Model Selection for Unsupervised Learning in High Dimensions. In: Proc. of NIPS, pp. 970–976 (1999)
Wallace, C.: Statistical and Inductive Inference by Minimum Message Length. Information Science and Statistics. Springer, Heidelberg (2005)
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Boutemedjet, S., Ziou, D., Bouguila, N. (2007). A Graphical Model for Content Based Image Suggestion and Feature Selection. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds) Knowledge Discovery in Databases: PKDD 2007. PKDD 2007. Lecture Notes in Computer Science(), vol 4702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74976-9_7
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