Learning logic rules for scene interpretation based on markov logic networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5996 LNCS, Issue PART 3, 2010, Pages 341-350

  Xu, Mai ^a   Petrou, Maria ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING CAPABILITIES; LOGIC RULES; MACHINE-LEARNING; MARKOV LOGIC NETWORKS; RULE LEARNING APPROACHES; SCENE INTERPRETATION;

NETWORK ARCHITECTURE;

COMPUTER VISION;

EID: 78650433697     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-12297-2_33     Document Type: Conference Paper

References (18)

1. Neumann, B., Möller, R.: On scene interpretation with description logics. Image and Vision Computing 26(1), 82-101 (2008)
2. Sudderth, E.B., Torralba, A., William, F.T., Willsky, A.S.: Describing visual scenes using transformed objects and parts. International Journal of Computer Vision 77(1-3), 291-330 (2008) (Pubitemid 351294745)
3. Thomas, A., Ferrari, V., Leibe, B., Tuytelaars, T., Schiele, B., Gool., L.V.: Towards multi-view object class detection. In: Proceedings of CVPR, pp. 1589-1596 (2006)
4. Heitz, G., Gould, S., Saxena, A., Koller, D.: Cascaded classification models: Combining models for holistic scene understanding. In: Proceedings of NIPS (2008)
5. Fei-fei, L., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: Proceedings of CVPR, pp. 524-531 (2005)
6. Komodakis, N., Tziritas, G., Paragios, N.: Fast approximately optimal solutions for single and dynamic MRFs. In: Proceedings of CVPR, pp. 1-8 (2007)
7. Petrou, M.: Learning in computer vision: some thoughts. In: Rueda, L., Mery, D., Kittler, J. (eds.) CIARP 2007. LNCS, vol. 4756, pp. 1-12. Springer, Heidelberg (2007)
8. Petrou, M., Xu, M.: The tower of knowledge scheme for learning in computer vision. In: Proceedings of DICTA 2007, pp. 85-91 (2007)
9. Xu, M., Petrou, M.: Recursive tower of knowledge for learning to interpret scenes. In: Proceedings of BMVC (2008)
10. Yakimovsky, Y., Feldman, J.: A semantics-based decision theory region analyzer. In: Proceedings of IJCAI, pp. 580-588 (1973)
11. Ohta, Y.: Knowledge-based interpretation of outdoor natural color scenes. Pitman Publishing (1985)
12. Han, F., Zhu, S.: Bottom-up/top-down image parsing with attribute graph grammar. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(1), 59-73 (2009)
13. Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62(1-2), 107-136 (2006)
14. Mitchell, T.M.: Machine Learning, 1st edn. McGraw-Hill Higher Education, New York (1997)
15. Quinlan, J.R., Cameron-Jones, M.,, R.: Foil: A midterm report. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 3-20. Springer, Heidelberg (1993)
16. IST06: E-training for interpreting images of man-made scenes, http://www.ipb.uni-bonn.de/projects/etrims/
17. IMPACT: The image processing for automatic cartographic tools project, http://www.robots.ox.ac.uk/~impact
18. Dick, A.R., Torr, P.H.S., Cipolla, R.: Modelling and interpretation of architecture from several images. International Journal of Computer Vision 60(2), 111-134 (2004)