Exploiting Contextual Knowledge for Hybrid Classification of Visual Objects

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10021)


We consider the problem of classifying visual objects in a scene by exploiting the semantic context. For this task, we define hybrid classifiers (HC) that combine local classifiers with context constraints, and can be applied to collective classification problems (CCPs) in general. Context constraints are represented by weighted ASP constraints using object relations. To integrate probabilistic information provided by the classifier and the context, we embed our encoding in the formalism \(LP^{MLN}\), and show that an optimal labeling can be efficiently obtained from the corresponding \(LP^{MLN}\) program by employing an ordinary ASP solver. Moreover, we describe a methodology for constructing an HC for a CCP, and present experimental results of applying an HC for object classification in indoor and outdoor scenes, which exhibit significant improvements in terms of accuracy compared to using only a local classifier.


  1. 1.
    Angin, P., Bhargava, B.: A confidence ranked co-occurrence approach for accurate object recognition in highly complex scenes. J. Internet Technol. 14(1), 13–19 (2013)Google Scholar
  2. 2.
    Buccafurri, F., Leone, N., Rullo, P.: Enhancing disjunctive datalog by constraints. IEEE Trans. Knowl. Data Eng. 12(5), 845–860 (2000)CrossRefGoogle Scholar
  3. 3.
    Chechetka, A., Dash, D., Philipose, M.: Relational learning for collective classification of entities in images. In: AAAI Workshops on Statistical Relational Artificial Intelligence, AAAI Workshop 2010. AAAI (2010)Google Scholar
  4. 4.
    Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV 2004, pp. 1–22 (2004)Google Scholar
  5. 5.
    Eiter, T., Ianni, G., Schindlauer, R., Tompits, H.: A uniform integration of higher-order reasoning and external evaluations in answer-set programming. In: International Joint Conference on Artificial Intelligence, IJCAI 2005, pp. 90–96. Professional Book Center (2005)Google Scholar
  6. 6.
    Galleguillos, C., Rabinovich, A., Belongie, S.J.: Object categorization using co-occurrence, location and appearance. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2008. IEEE Computer Society (2008)Google Scholar
  7. 7.
    Gebser, M., Kaufmann, B., Kaminski, R., Ostrowski, M., Schaub, T., Schneider, M.T.: Potassco: the potsdam answer set solving collection. AI Commun. 24(2), 107–124 (2011)MathSciNetMATHGoogle Scholar
  8. 8.
    Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Gener. Comput. 9(3/4), 365–386 (1991)CrossRefMATHGoogle Scholar
  9. 9.
    Getoor, L.: Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)MATHGoogle Scholar
  10. 10.
    Lee, J., Wang, Y.: Weighted rules under the stable model semantics. In: Proceedings of the Fifteenth International Conference on Principles of Knowledge Representation and Reasoning, KR 2016, pp. 145–154. AAAI Press (2016)Google Scholar
  11. 11.
    London, B., Getoor, L.: Collective classification of network data. In: Aggarwal, C.C. (ed.) Data Classification: Algorithms and Applications, pp. 399–416. CRC Press, Boca Raton (2014)Google Scholar
  12. 12.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: 7th IEEE International Conference on Computer Vision, ICCV 1999, pp. 1150–1157 (1999)Google Scholar
  13. 13.
    Marton, Z.C., Rusu, R.B., Jain, D., Klank, U., Beetz, M.: Probabilistic categorization of kitchen objects in table settings with a composite sensor. In: International Conference on Intelligent Robots and Systems, IEEE/RSJ 2009, pp. 4777–4784. IEEE (2009)Google Scholar
  14. 14.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., VanderPlas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATHGoogle Scholar
  15. 15.
    Rabinovich, A., Belongie, S.J.: Scenes vs. objects: a comparative study of two approaches to context based recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR Workshops 2009, pp. 92–99. IEEE Computer Society (2009)Google Scholar
  16. 16.
    Rabinovich, A., Vedaldi, A., Galleguillos, C., Wiewiora, E., Belongie, S.J.: Objects in context. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8. IEEE Computer Society (2007)Google Scholar
  17. 17.
    Randell, D.A., Cui, Z., Cohn, A.G.: A spatial logic based on regions and connection. In: Proceedings of the 3rd International Conference on Principles of Knowledge Representation and Reasoning, KR 1992, pp. 165–176. Morgan Kaufmann (1992)Google Scholar
  18. 18.
    Richardson, M., Domingos, P.M.: Markov logic networks. Mach. Learn. 62(1–2), 107–136 (2006)CrossRefGoogle Scholar
  19. 19.
    Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: a database and web-based tool for image annotation. Int. J. Comput. Vis. 77(1–3), 157–173 (2008)CrossRefGoogle Scholar
  20. 20.
    Saathoff, C., Staab, S.: Exploiting spatial context in image region labelling using fuzzy constraint reasoning. In: Ninth International Workshop on Image Analysis for Multimedia Interactive Services, WIAMIS 2008, pp. 16–19. IEEE Computer Society (2008)Google Scholar
  21. 21.
    Sen, P., Namata, G., Bilgic, M., Getoor, L.: Collective classification. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 189–193. Springer, US (2010)Google Scholar
  22. 22.
    Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93–106 (2008)Google Scholar
  23. 23.
    Strobl, C.: Dimensionally extended nine-intersection model (DE-9IM). In: Shekhar, S., Xiong, H. (eds.) Encyclopedia of GIS, pp. 240–245. Springer, US (2008)CrossRefGoogle Scholar
  24. 24.
    Tran, S.D., Davis, L.S.: Event modeling and recognition using Markov logic networks. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5303, pp. 610–623. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-88688-4_45 CrossRefGoogle Scholar
  25. 25.
    Zhang, H., Fritts, J.E., Goldman, S.A.: Image segmentation evaluation: a survey of unsupervised methods. Comput. Vis. Image Underst. 110(2), 260–280 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Institute of Information SystemsTU WienViennaAustria

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