Video Hunter at VBS 2017

  • Adam Blaz̆ekEmail author
  • Jakub Lokoc̆
  • David Kubon̆
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10133)


After almost three years of development, the Video Hunter tool (formerly the Signature-Based Video Browser) has become a complex tool combining different query modalities, multi-sketches, visualizations and browsing techniques. In this paper, we present additional improvements of the tool focusing on keyword search. More specifically, we present a method relying on an external image search engine and a method relying on ImageNet labels. We also present a keyframe caching method employed by our tool.



This research was supported by grant SVV-2016-260331 and GAUK project no. 1134316. We would also like to thank Jan Pavlovský for helping us with 2D image maps.


  1. 1.
    Barthel, K.U., Hezel, N., Mackowiak, R.: Navigating a graph of scenes for exploring large video collections. In: Tian, Q., Sebe, N., Qi, G.-J., Huet, B., Hong, R., Liu, X. (eds.) MMM 2016. LNCS, vol. 9517, pp. 418–423. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-27674-8_43 CrossRefGoogle Scholar
  2. 2.
    Blažek, A., Lokoč, J., Skopal, T.: Video retrieval with feature signature sketches. In: Traina, A.J.M., Traina, C., Cordeiro, R.L.F. (eds.) SISAP 2014. LNCS, vol. 8821, pp. 25–36. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-11988-5_3 Google Scholar
  3. 3.
    Cobârzan, C., Schoeffmann, K., Bailer, W., Hürst, W., Blažek, A., Lokoč, J., Vrochidis, S., Barthel, K.U., Rossetto, L.: Interactive video search tools: a detailed analysis of the video browser showdown 2015. Multimedia Tools Appl., 1–33 (2016)Google Scholar
  4. 4.
    Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T., Decaf: a deep convolutional activation feature for generic visual recognition. CoRR, abs/1310.1531 (2013)Google Scholar
  5. 5.
    Hudelist, M.A., Cobârzan, C., Beecks, C., Werken, R., Kletz, S., Hürst, W., Schoeffmann, K.: Collaborative video search combining video retrieval with human-based visual inspection. In: Tian, Q., Sebe, N., Qi, G.-J., Huet, B., Hong, R., Liu, X. (eds.) MMM 2016. LNCS, vol. 9517, pp. 400–405. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-27674-8_40 CrossRefGoogle Scholar
  6. 6.
    Kuboň, D., Blažek, A., Lokoč, J., Skopal, T.: Multi-sketch semantic video browser. In: Tian, Q., Sebe, N., Qi, G.-J., Huet, B., Hong, R., Liu, X. (eds.) MMM 2016. LNCS, vol. 9517, pp. 406–411. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-27674-8_41 CrossRefGoogle Scholar
  7. 7.
    Lokoč, J., Blažek, A., Skopal, T.: Signature-based video browser. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds.) MMM 2014. LNCS, vol. 8326, pp. 415–418. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-04117-9_49 CrossRefGoogle Scholar
  8. 8.
    Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  9. 9.
    Russakovsky, O., Deng, J., Hao, S., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Schoeffmann, K.: A user-centric media retrieval competition: the video browser showdown 2012–2014. IEEE MultiMedia 21(4), 8–13 (2014)CrossRefGoogle Scholar
  11. 11.
    Schoeffmann, K., Hudelist, M.A., Huber, J.: Video interaction tools: a survey of recent work. ACM Comput. Surv. 48(1), 14 (2015)CrossRefGoogle Scholar
  12. 12.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.SIRET Research Group, Department of Software Engineering, Faculty of Mathematics and PhysicsCharles UniversityPragueCzech Republic

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