Multi-label Classification of Movie Posters into Genres with Rakel Ensemble Method

  • Marina Ivasic-KosEmail author
  • Miran Pobar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10630)


Movies can belong to more than one genre, so the problem of determining the genres of a movie from its poster is a multi-label classification problem. To solve the multi-label problem, we have used the RAKEL ensemble method along with three typical single-label base classification methods: Naïve Bayes, C4.5 decision tree, and k-NN. The RAKEL method strives to overcome the problem of computational cost and power set label explosion by breaking the initial set of labels into several small-sized label sets.

The classification performance of base classifiers on different feature sets is evaluated using multi-label evaluation measures on poster dataset containing 6000 posters classified into 18 and 11 genres.

Keeping this in mind, we wanted to examine how different visual feature sets, extracted from poster images, are related to the performance of automatic detection of movie genres, as well as compare it to the performance obtained with the Classeme feature descriptors trained on the datasets of general images.


Multi-label classification RAKEL ensemble method Movie poster Classemes GIST 



This research was fully supported by Croatian Science Foundation under the project Automatic recognition of actions and activities in multimedia content from the sports domain (RAASS).


  1. 1.
    The movie database, March 2014.
  2. 2.
    Madjarov, G., Kocev, D., Gjorgjevikj, D., Džeroski, S.: An extensive experimental comparison of methods for multi-label learning. Pattern Recognit. 45(9), 3084–3104 (2012)CrossRefGoogle Scholar
  3. 3.
    Rasheed, Z., Sheikh, Y., Shah, M.: On the use of computable features for film classification. IEEE Trans. Circ. Syst. Video Technol. 15(1), 52–64 (2005)CrossRefGoogle Scholar
  4. 4.
    Huang, H.-Y., Shih, W.-S., Hsu, W.-H.: A film classifier based on low-level visual features. In: IEEE 9th Workshop on Multimedia Signal Processing, MMSP 2007, pp. 465–468. IEEE (2007)Google Scholar
  5. 5.
    Zhou, H., Hermans, T., Karandikar, A.V., Rehg, J.M.: Movie genre classification via scene categorization. In: Proceedings of the International Conference on Multimedia, pp. 747–750. ACM (2007)Google Scholar
  6. 6.
    Ivašić-Kos, M., Pobar, M., Mikec, L.: Movie posters classification into genres based on low-level features. In: Proceedings of International Conference MIPRO, Opatija (2014)Google Scholar
  7. 7.
    Ivasic-Kos, M., Pobar, M., Ipsic, I.: Automatic movie posters classification into genres. In: Bogdanova, A.M., Gjorgjevikj, D. (eds.) ICT Innovations 2014. AISC, vol. 311, pp. 319–328. Springer, Cham (2015). Google Scholar
  8. 8.
    Fu, Z., Li, B., Li, J., Wei, S.: Fast film genres classification combining poster and synopsis. In: He, X., Gao, X., Zhang, Y., Zhou, Z.-H., Liu, Z.-Y., Fu, B., Hu, F., Zhang, Z. (eds.) IScIDE 2015. LNCS, vol. 9242, pp. 72–81. Springer, Cham (2015). CrossRefGoogle Scholar
  9. 9.
    Pobar, M., Ivasic-Kos, M.: Multi-label poster classification into genres using different problem transformation methods. In: Felsberg, M., Heyden, A., Krüger, N. (eds.) CAIP 2017. LNCS, vol. 10425, pp. 367–378. Springer, Cham (2017). CrossRefGoogle Scholar
  10. 10.
    Torresani, L., Szummer, M., Fitzgibbon, A.: Efficient object category recognition using classemes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 776–789. Springer, Heidelberg (2010). CrossRefGoogle Scholar
  11. 11.
    Tsoumakas, G., Vlahavas, I.: Random k-labelsets: an ensemble method for multilabel classification. In: Kok, J.N., Koronacki, J., de Mantaras, R.L., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS, vol. 4701, pp. 406–417. Springer, Heidelberg (2007). CrossRefGoogle Scholar
  12. 12.
    Zhang, M.-L., Zhou, Z.-H.: A k-nearest neighbor based algorithm for multi-label classification. In: 2005 IEEE International Conference on Granular Computing, vol. 2 (2005)Google Scholar
  13. 13.
    Mitchell, T.M.: Machine Learning. McGraw Hill, Burr Ridge (1997)zbMATHGoogle Scholar
  14. 14.
    Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehoue. Min. 3(3), 1–13 (2007)CrossRefGoogle Scholar
  15. 15.
    Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. J. 85(3), 333–359 (2011). SpringerMathSciNetCrossRefGoogle Scholar
  16. 16.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)CrossRefzbMATHGoogle Scholar
  17. 17.
    Shechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: IEEE CVPR 2007, pp. 1–8 (2007)Google Scholar
  18. 18.
    Yang, Y.: An evaluation of statistical approaches to text categorization. Inf. Retrieval 1(1–2), 69–90 (1999)CrossRefGoogle Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of InformaticsUniversity of RijekaRijekaCroatia

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