• DocumentCode
    1967134
  • Title

    Movies genres classifier using neural network

  • Author

    Jain, Sanjay K. ; Jadon, R.S.

  • Author_Institution
    Dept. of MCA, Inst. of Technol. & Manage., Gwalior, India
  • fYear
    2009
  • fDate
    14-16 Sept. 2009
  • Firstpage
    575
  • Lastpage
    580
  • Abstract
    In this paper we have designed a neural network based movie genres classifier. The Movie classifier characterizes the movie clips into different movie genres. The characterization is based on low level audio-visual features. We have extracted the computable audio-visual features from the movie clips which are inspired by the techniques and film grammars used by many filmmakers to endow specific characteristics to a genre. The extracted visual features are shot length, motion, color dominance and lighting key and the extracted audio features are based on time domain, pitch, frequency domain, energy and MFCC. Movie classifier is designed using feed forward neural network with back propagation learning algorithm. We have demonstrated the effectiveness of the classifier for characterizing the movie clips into action, horror, comedy, music and drama genres.
  • Keywords
    audio signal processing; audio-visual systems; backpropagation; cinematography; feature extraction; feedforward neural nets; humanities; image classification; video signal processing; audio feature extraction; audio-visual feature; back propagation learning algorithm; digital video; feed forward neural network; film grammar; filmmaker; movie clip; movie genre classifier; time domain; visual feature extraction; Availability; Computer network management; Feature extraction; Internet; Mood; Motion pictures; Neural networks; TV; Video compression; Video on demand; audio-visual features; movie genres classifier; neural netowrk;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Information Sciences, 2009. ISCIS 2009. 24th International Symposium on
  • Conference_Location
    Guzelyurt
  • Print_ISBN
    978-1-4244-5021-3
  • Electronic_ISBN
    978-1-4244-5023-7
  • Type

    conf

  • DOI
    10.1109/ISCIS.2009.5291884
  • Filename
    5291884