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A Concept of Visual Knowledge Representation

  • Tatiana JaworskaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 833)

Abstract

The image semantic representation is a very challenging task. This article presents a concept of using visual analysis to represent knowledge based on large amounts of massive, dynamic, ambiguous multimedia. This concept is based on the semantic representation of these visual resources. We argue that the most important factor in building a semantic representation is defining the ordered and hierarchical structure, as well as the relationships among entities. This concept has stemmed from the content-based image retrieval analysis.

Keywords

Image semantics Knowledge representation Order Similarity 

References

  1. 1.
    Belongie, S., Perona, P.: Visipedia circa 2015. Pattern Recogn. Lett. 72, 15–24 (2016).  https://doi.org/10.1016/j.patrec.2015.11.023
  2. 2.
    Hawking, J., Blakeslee, J.: On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines, p. 262. Henry Holt and Company, New York (2004)Google Scholar
  3. 3.
    Poston, T., Stewart, I.: Nonlinear modeling of multistable perception. Syst. Res. Behav. Sci. 4(23), 318–334 (1978).  https://doi.org/10.1002/bs.3830230403MathSciNetCrossRefGoogle Scholar
  4. 4.
    Yu, F., Pedrycz, W.: The design of fuzzy information granules: tradeoffs between specificity and experimental evidence. Appl. Soft Comput. 9, 264–273 (2009).  https://doi.org/10.1016/j.asoc.2007.10.026CrossRefGoogle Scholar
  5. 5.
    Chmiel, P., Ganzha, M., Jaworska, T., Paprzycki, M.: Combining semantic technologies with a content-based image retrieval system – preliminary considerations. In: Application of Mathematics in Technical and Natural Sciences, Conference Proceedings, Albena, Bulgaria, pp. 100001–1000012 (2017).  https://doi.org/10.1063/1.5007405
  6. 6.
    Jaworska, T.: The concept of a multi-step search-engine for the content-based image retrieval systems. In: Information Systems Architecture and Technology. Web Information Systems Engineering, Knowledge Discovery and Hybrid Computing, Wrocław, pp. 189–200 (2011)Google Scholar
  7. 7.
    Jaworska, T.: A search-engine concept based on multi-feature vectors and spatial relationship. In: Christiansen, H., De Tré, G., Yazici, A., Zadrożny, S., Larsen, H.L. (eds.) Flexible Query Answering Systems, vol. 7022, pp. 137–148. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-24764-4_13
  8. 8.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems – NIPS 2012, Lake Tahoe, Nevada, USA, 03–06 December 2012, pp. 1097–1105 (2012)Google Scholar
  9. 9.
    Zhang, N., Donahue, J., Girshick, R., Darrell, T.: Part-based R-CNNs for fine-grained category detection. In: 13th European Conference on Computer Vision - ECCV. Proceedings, Part I, Zurich, Switzerland, 6–12 September 2014, pp. 834–849 (2014).  https://doi.org/10.1007/978-3-319-10590-1_54
  10. 10.
    Simovici, D.A., Djeraba, C.: Partially ordered sets. In: Mathematical Tools for Data Mining: Set Theory, Partial Orders, Combinatorics, p. 615. Springer, London (2008).  https://doi.org/10.1007/978-1-4471-6407-4
  11. 11.
    Freese, R.: Automated lattice drawing. In: Second International Conference on Formal Concept Analysis, Sydney, Australia, 23–26 February 2004, pp. 580–596 (2004).  https://doi.org/10.1007/978-3-540-24651-0_12
  12. 12.
    Bang-Jensen, J., Gutin, G.Z.: Basic terminology, notation and results. In: Digraphs Theory, Algorithms and Applications, pp. 1–30. Springer, London (2009).  https://doi.org/10.1007/978-1-84800-998-1
  13. 13.
    Bart, E., Porteous, I., Perona, P., Welling, M.: Unsupervised learning of visual taxonomies. In: IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, USA, 23–28 June 2008, pp. 1–8, (2008)Google Scholar
  14. 14.
    Stevens, S.S.: On the theory of scales of measurement. Science 103(2684), 677–680 (1946).  https://doi.org/10.1126/science.103.2684.677
  15. 15.
    Rozeboom, W.W.: Scaling theory and the nature of measurement. Synthese 16(2), 170–233 (1966).  https://doi.org/10.1007/bf00485356
  16. 16.
    Manolopoulos, Y., Nanopoulos, A., Papadopoulos, A.N., Theodoridis, Y.: R-Trees: Theory and Applications, 3rd edn., p. 194. Springer, London (2010).  https://doi.org/10.1007/978-1-84628-293-5

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Systems Research InstitutePolish Academy of SciencesWarsawPoland

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