Measuring Scene Detection Performance

  • Lorenzo BaraldiEmail author
  • Costantino Grana
  • Rita Cucchiara
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9117)


In this paper we evaluate the performance of scene detection techniques, starting from the classic precision/recall approach, moving to the better designed coverage/overflow measures, and finally proposing an improved metric, in order to solve frequently observed cases in which the numeric interpretation is different from the expected results. Numerical evaluation is performed on two recent proposals for automatic scene detection, and comparing them with a simple but effective novel approach. Experimental results are conducted to show how different measures may lead to different interpretations.


Scene detection Measures Clustering 



This work was carried out within the project “Città educante” (CTN01_00034_393801) of the National Technological Cluster on Smart Communities co funded by the Italian Ministry of Education, University and Research - MIUR.


  1. 1.
    Apostolidis, E., Mezaris, V.: Fast shot segmentation combining global and local visual descriptors. In: IEEE International Conference on Acoustics, Speech and Signal Process, pp. 6583–6587 (2014)Google Scholar
  2. 2.
    Baraldi, L., Paci, F., Serra, G., Benini, L., Cucchiara, R.: Gesture recognition in ego-centric videos using dense trajectories and hand segmentation. In: Proceedings of 10th IEEE Embedded Vision Workshop (EVW), Columbus, Ohio, June 2014Google Scholar
  3. 3.
    Bertini, M., Del Bimbo, A., Serra, G., Torniai, C., Cucchiara, R., Grana, C., Vezzani, R.: Dynamic pictorially enriched ontologies for video digital libraries. IEEE MultiMedia 16(2), 41–51 (2009)CrossRefGoogle Scholar
  4. 4.
    Chasanis, V.T., Likas, C., Galatsanos, N.P.: Scene detection in videos using shot clustering and sequence alignment. IEEE Trans. Multimedia 11(1), 89–100 (2009)CrossRefGoogle Scholar
  5. 5.
    Hanjalic, A., Lagendijk, R.L., Biemond, J.: Automated high-level movie segmentation for advanced video-retrieval systems. IEEE Trans. Circuits Syst. Video Technol. 9(4), 580–588 (1999)CrossRefGoogle Scholar
  6. 6.
    Rasheed, Z., Shah, M.: Detection and representation of scenes in videos. IEEE Trans. Multimedia 7(6), 1097–1105 (2005)CrossRefGoogle Scholar
  7. 7.
    Sidiropoulos, P., Mezaris, V., Kompatsiaris, I., Meinedo, H., Bugalho, M., Trancoso, I.: Temporal video segmentation to scenes using high-level audiovisual features. IEEE Trans. Circuits Syst. Video Technol. 21(8), 1163–1177 (2011)CrossRefGoogle Scholar
  8. 8.
    Vendrig, J., Worring, M.: Systematic evaluation of logical story unit segmentation. IEEE Trans. Multimedia 4(4), 492–499 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Lorenzo Baraldi
    • 1
    Email author
  • Costantino Grana
    • 1
  • Rita Cucchiara
    • 1
  1. 1.Dipartimento di Ingegneria “Enzo Ferrari”Università degli Studi di Modena e Reggio EmiliaModenaItaly

Personalised recommendations