Automatic Video Summarization Using the Optimum-Path Forest Unsupervised Classifier

  • César Castelo-FernándezEmail author
  • Guillermo Calderón-Ruiz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9423)


In this paper a novel method for video summarization is presented, which uses a color-based feature extraction technique and a graph-based clustering technique. One major advantage of this method is that it is parameter-free, that is, we do not need to define neither the number of shots or a consecutive-frames dissimilarity threshold. The results have shown that the method is both effective and efficient in processing videos containing several thousands of frames, obtaining very meaningful summaries in a quick way.


Optimum-path forest classifier Video summarization Shot detection Clustering Video processing 


  1. 1.
    Castelo-Fernández, C.: Content-based video retrieval through wavelets and clustering. In: Proceedings of the IV Workshop de Visão Computacional, Bauru, São Paulo, Brasil. UNESP (2008)Google Scholar
  2. 2.
    Chen, L.H., Su, C.W., Mark Liao, H.Y., Shih, C.C.: On the preview of digital movies. Journal of Visual Communication and Image Representation (2003)Google Scholar
  3. 3.
    Ejaz, N.: Tayyab Bin Tariq, and Sung Wook Baik. Adaptive key frame extraction for video summarization using an aggregation mechanism. Journal of Visual Communication and Image Representation 23(7), 1031–1040 (2012)CrossRefGoogle Scholar
  4. 4.
    Jadhav, P.S., Jadhav, D.S.: Video summarization using higher order color moments (vsuhcm). Procedia Computer Science 45, 275–281 (2015). International Conference on Advanced Computing Technologies and Applications (ICACTA)CrossRefGoogle Scholar
  5. 5.
    Pfeiffer, S., Lienhart, R., Fischer, S., Effelsberg, W.: Abstracting digital movies automatically. Journal of Visual Communication and Image Representation 7(4), 345–353 (1996)CrossRefGoogle Scholar
  6. 6.
    Ren, J., Jiang, J., Feng, Y.: Activity-driven content adaptation for effective video summarization. Journal of Visual Communication and Image Representation 21(8), 930–938 (2010). Large-Scale Image and Video Search: Challenges, Technologies, and TrendsCrossRefGoogle Scholar
  7. 7.
    Rocha, L.M., Cappabianco, F.A.M., Falcão, A.X.: Data clustering as an optimum-path forest problem with applications in image analysis. Int. J. Imaging Syst. Technol. 19, 50–68 (2009)CrossRefGoogle Scholar
  8. 8.
    Stehling, R.O., Nascimento, M.A., Falcão, A.X.: A compact and efficient image retrieval approach based on border/interior pixel classification. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management, CIKM ’02, pp. 102–109 (2002)Google Scholar
  9. 9.
    Zhou, H., Sadka, A.H., Swash, M.R., Azizi, J., Sadiq, U.A.: Feature extraction and clustering for dynamic video summarisation. Neurocomputing 73(10–12), 1718–1729 (2010). Subspace Learning/Selected papers from the European Symposium on Time Series PredictionCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • César Castelo-Fernández
    • 1
    Email author
  • Guillermo Calderón-Ruiz
    • 1
  1. 1.School of Systems EngineeringSanta María Catholic UniversityArequipaPeru

Personalised recommendations