Semantic Gap in Image and Video Analysis: An Introduction

  • Halina KwaśnickaEmail author
  • Lakhmi C. Jain
Part of the Intelligent Systems Reference Library book series (ISRL, volume 145)


The chapter presents a brief introduction to the problem with the semantic gap in content-based image retrieval systems. It presents the complex process of image processing, leading from raw images, through subsequent stages to the semantic interpretation of the image. Next, the content of all chapters included in this book is shortly presented.


  1. 1.
    Alzubaidi, M.A., Narrowing the semantic gap in natural images. In: 5th International Conference on Information and Communication Systems (ICICS), Irbid, 2014, pp. 1–6 (2014).
  2. 2.
    Alzubaidi, M.A.: A new strategy for bridging the semantic gap in image retrieval. Int. J. Comput. Sci. Eng. (IJCSE) 14(1) (2017)Google Scholar
  3. 3.
    Jaimes, A., Christel, M., Gilles, S., Sarukkai, R., Ma, W.-Y.: Multimedia information retrieval: what is it, and why isn’t anyone using it? In: Proceeding MIR 2005, Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, Hilton, Singapore, pp. 3–8 (2005)Google Scholar
  4. 4.
    Luke, K.-K, Liu, H.-L, Wai, Y.-Y., Wan, Y.-L., Tan, L.H.: Functional anatomy of syntactic and semantic processing in language comprehension. Hum. Brain Mapp. 16(3), 133–145 (2002)Google Scholar
  5. 5.
    Luo, J., Crandall, D., Singhal, A., Boutell, M., Gray, R.T.: Psychophysical study of image orientation perception. Spat. Vis. 16(5), 429457 (2003)Google Scholar
  6. 6.
    Friedrich R.M., Friederici A.D.: Mathematical logic in the human brain: semantics. PLoS ONE 8(1), e53699 (2013).
  7. 7.
    Rommers, J., Dijkstra, T., Bastiaansen, M.: Context-dependent semantic processing in the human brain: evidence from idiom comprehension. J. Cogn. Neurosci. 25(5), 762–776 (2013)Google Scholar
  8. 8.
    Mitchell, D.J., Cusack, R.: Semantic and emotional content of imagined representations in human occipitotemporal cortex. Sci. Rep. 6, 20232 (2016).
  9. 9.
    Tomasello, R., Garagnani, M., Wennekers, T., Pulvermller, F.: Brain connections of words, perceptions and actions: a neurobiological model of spatio-temporal semantic activation in the human cortex. Neuropsychol. 98, 111–129 (2017)Google Scholar
  10. 10.
    Shrivastava, P., Bhoyar, K.K., Zadgaonkar, A.S.: Bridging the semantic gap with human perception based features for scene categorization. Int. J. Intell. Comput. Cybern. 10(3), 387–406 (2017)Google Scholar
  11. 11.
    Colombino, T., Martin, D., Grasso, A., Marchesotti, L.: Reformulation of the semantic gap problem in content-based image retrieval scenarios. In: Lewkowicz, M. et al. (eds.) Proceedings of COOP 2010, Computer Supported Cooperative Work, Springer (2010)Google Scholar
  12. 12.
    Li, Y., Leung, C.H.C.: Multi-level semantic characterization and re-finement for web image search. Procedia Environ. Sci. 11, 147–154 (2011). (Elsevier Ltd.)
  13. 13.
    Li, X., Uricchio, T., Ballan, L., Bertini, M., M. Snoek, C.G., Bimbo, A.D.: Socializing the semantic gap: a comparative survey on image tag assignment, refinement, and retrieval. ACM Comput. Surv. 49(1), 14 (2016)Google Scholar
  14. 14.
    Alzu’bi, A., Amira, A., Ramzan, N.: Semantic content-based image retrieval: a comprehensive study. J. Vis. Commun. Image Represent. 32, 20–54 (2015)Google Scholar
  15. 15.
    Mesnil, G., Bordes, A., Weston, J., Chechik, G., Bengio, Y.: Learning semantic representations of objects and their parts. Mach. Learn. 94(2), 281–301 (2014)Google Scholar
  16. 16.
    Singh, S., Sontakke, T.: An effective mechanism to neutralize the semantic gap in Content Based Image Retrieval (CBIR). Int. Arab J. Inf. Technol. 11(2) (2014)Google Scholar
  17. 17.
    Montazer, G.A., Giveki, D.: Content based image retrieval system using clustered scale invariant feature transforms. Optik—Int. J. Light Electron Opt. 126(18), 1695–1699 (2015)Google Scholar
  18. 18.
    Srivastava, P., Khare, A.: Integration of wavelet transform, Local Binary Patterns and moments for content-based image retrieval. J. Vis. Commun. Image Represent. 42, 78–103 (2017)Google Scholar
  19. 19.
    Dong, H., Yu, S., Wu, C., Guo, Y.: Semantic Image Synthesis via Adversarial Learning. Accepted to ICCV 2017, Subjects: Computer Vision and Pattern Recognition (cs.CV), arXiv:1707.06873v1 [cs.CV] (2017)
  20. 20.
    Yasmin, M., Mohsin, S., Sharif, M.: Intelligent image retrieval techniques: a survey. J. Appl. Res. Technol. 12(1), 87–103 (2014)Google Scholar
  21. 21.
    Khodaskar, A., Ladhake, S.: Semantic image analysis for intelligent image retrieval. Procedia Comput. Sci. 48, 192–197 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Computational IntelligenceWroclaw University of Science and TechnologyWroclawPoland
  2. 2.Founder, KES InternationalLeedsUK
  3. 3.Faculty of Science, Technology and MathematicsUniversity of CanberraCanberraAustralia

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