Advertisement

A Comparative Study for Known Item Visual Search Using Position Color Feature Signatures

  • Jakub LokočEmail author
  • David Kuboň
  • Adam Blažek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10133)

Abstract

According to the results of the Video Browser Showdown competition, position-color feature signatures proved to be an effective model for visual known-item search tasks in BBC video collections. In this paper, we investigate details of the retrieval model based on feature signatures, given a state-of-the-art known item search tool – Signature-based Video Browser. We also evaluate a preliminary comparative study for three variants of the utilizes distance measures. In the discussion, we analyze logs and provide clues for understanding the performance of our model.

Keywords

Similarity search Feature extraction Known item search Color sketch 

Notes

Acknowledgments

This research was supported by grant SVV-2016-260331, Charles University project P46 and GAUK project no. 1134316.

References

  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.
    Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)CrossRefzbMATHGoogle Scholar
  3. 3.
    Blažek, A., Lokoč, J., Matzner, F., Skopal, T.: Enhanced signature-based video browser. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds.) MMM 2015. LNCS, vol. 8936, pp. 243–248. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-14442-9_22 Google Scholar
  4. 4.
    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
  5. 5.
    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
  6. 6.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Surv. 40(2), 5:1–5:60 (2008)CrossRefGoogle Scholar
  7. 7.
    Fabro, M., Böszörmenyi, L.: AAU video browser: non-sequential hierarchical video browsing without content analysis. In: Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, C.-W., Andreopoulos, Y., Breiteneder, C. (eds.) MMM 2012. LNCS, vol. 7131, pp. 639–641. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-27355-1_63 CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C., Bottou, L., Weinberger, K. (eds.) Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, Lake Tahoe, Nevada, US, 3–6 December 2012, pp. 1097–1105. Curran Associates, Inc. (2012)Google Scholar
  10. 10.
    Kruliš, M., Lokoč, J., Skopal, T.: Efficient extraction of clustering-based feature signatures using GPU architectures. Multimedia Tools Appl. 75(13), 8071–8103 (2016)CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Le, D.-D., Lam, V., Ngo, T.D., Tran, V.Q., Nguyen, V.H., Duong, D.A., Satoh, S.: NII-UIT-VBS: a video browsing tool for known item search. In: Li, S., Saddik, A., Wang, M., Mei, T., Sebe, N., Yan, S., Hong, R., Gurrin, C. (eds.) MMM 2013. LNCS, vol. 7733, pp. 547–549. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-35728-2_65 CrossRefGoogle Scholar
  13. 13.
    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
  14. 14.
    Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vis. 40(2), 99–121 (2000)CrossRefzbMATHGoogle Scholar
  15. 15.
    Schoeffmann, K.: A user-centric media retrieval competition: the video browser showdown 2012–2014. IEEE MultiMedia 21(4), 8–13 (2014)CrossRefGoogle Scholar
  16. 16.
    Schoeffmann, K., Hudelist, M.A., Huber, J.: Video interaction tools: a survey of recent work. ACM Comput. Surv. 48(1), 14 (2015)CrossRefGoogle Scholar
  17. 17.
    Smeaton, A.F., Over, P., Kraaij, W.: Evaluation campaigns and TRECVid. In: Proceedings of the 8th ACM International Workshop on Multimedia Information Retrieval, MIR 2006, pp. 321–330. ACM Press, New York (2006)Google Scholar
  18. 18.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9, June 2015Google 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 University in PraguePragueCzech Republic

Personalised recommendations