Advertisement

Revisiting SIRET Video Retrieval Tool

  • Jakub Lokoč
  • Gregor Kovalčík
  • Tomáš Souček
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10705)

Abstract

The known-item and ad-hoc video search tasks still represent challenging problems for the video retrieval community. During last years, the Video Browser Showdown identified several promising approaches that can improve the effectiveness of interactive video retrieval tools focusing on the tasks. We present a major revision of the SIRET interactive video retrieval tool that follows these findings. The new version employs three different query initialization approaches and provides several result visualization methods for effective navigation and browsing in sets of ranked keyframes.

Notes

Acknowledgments

This paper has been supported by Czech Science Foundation (GAČR) project Nr. 17-22224S and by grant SVV-2017-260451.

References

  1. 1.
    Amato, G., Falchi, F., Gennaro, C., Rabitti, F.: Searching and annotating 100M images with YFCC100M-HNFC6 and MI-file. In: Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing, CBMI 2017, New York, NY, USA, pp. 26:1–26:4. ACM (2017)Google Scholar
  2. 2.
    Awad, G., Butt, A., Fiscus, J., Michel, M., Joy, D., Kraaij, W., Smeaton, A.F., Quénot, G., Eskevich, M., Ordelman, R., Jones, G.J.F., Huet, B.: TRECVID 2017: evaluating ad-hoc and instance video search, events detection, video captioning and hyperlinking. In: Proceedings of TRECVID 2017. NIST (2017)Google Scholar
  3. 3.
    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, Cham (2016).  https://doi.org/10.1007/978-3-319-27674-8_43 CrossRefGoogle Scholar
  4. 4.
    Blaz̆ek, A., Lokoc̆, J., Kubon̆, D.: Video hunter at VBS 2017. In: Amsaleg, L., Guðmundsson, G.Þ., Gurrin, C., Jónsson, B.Þ., Satoh, S. (eds.) MMM 2017. LNCS, vol. 10133, pp. 493–498. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-51814-5_47 CrossRefGoogle Scholar
  5. 5.
    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, Cham (2014).  https://doi.org/10.1007/978-3-319-11988-5_3 Google Scholar
  6. 6.
    Budíková, P., Batko, M., Zezula, P.: Fusion strategies for large-scale multi-modal image retrieval. Ttans. Large-Scale Data Knowl.-Centered Syst. 33, 146–184 (2017)Google Scholar
  7. 7.
    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. Multimed. Tools Appl. 76(4), 5539–5571 (2016)CrossRefGoogle Scholar
  8. 8.
    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, Cham (2014).  https://doi.org/10.1007/978-3-319-04117-9_49 CrossRefGoogle Scholar
  9. 9.
    Lokoč, J., Phuong, A.N., Vomlelová, M., Ngo, C.-W.: Color-sketch simulator: a guide for color-based visual known-item search. In: Cong, G., Peng, W.-C., Zhang, W.E., Li, C., Sun, A. (eds.) ADMA 2017. LNCS (LNAI), vol. 10604, pp. 754–763. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69179-4_53 CrossRefGoogle Scholar
  10. 10.
    Rossetto, L., Giangreco, I., Tănase, C., Schuldt, H., Dupont, S., Seddati, O.: Enhanced retrieval and browsing in the IMOTION system. In: Amsaleg, L., Guðmundsson, G.Þ., Gurrin, C., Jónsson, B.Þ., Satoh, S. (eds.) MMM 2017. LNCS, vol. 10133, pp. 469–474. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-51814-5_43 CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Schoeffmann, K.: A user-centric media retrieval competition: the video browser showdown 2012–2014. IEEE Multimed. 21(4), 8–13 (2014)CrossRefGoogle Scholar
  13. 13.
    Schoeffmann, K., Hudelist, M.A., Huber, J.: Video interaction tools: a survey of recent work. ACM Comput. Surv. 48(1), 14 (2015)CrossRefGoogle Scholar
  14. 14.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556 (2014)Google Scholar
  15. 15.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, 7–12 June 2015, pp. 1–9 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Jakub Lokoč
    • 1
  • Gregor Kovalčík
    • 1
  • Tomáš Souček
    • 1
  1. 1.SIRET Research Group, Department of Software Engineering, Faculty of Mathematics and PhysicsCharles UniversityPragueCzech Republic

Personalised recommendations