• DocumentCode
    952136
  • Title

    Affective understanding in film

  • Author

    Wang, Hee Lin ; Cheong, Loong-Fah

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore
  • Volume
    16
  • Issue
    6
  • fYear
    2006
  • fDate
    6/1/2006 12:00:00 AM
  • Firstpage
    689
  • Lastpage
    704
  • Abstract
    Affective understanding of film plays an important role in sophisticated movie analysis, ranking and indexing. However, due to the seemingly inscrutable nature of emotions and the broad affective gap from low-level features, this problem is seldom addressed. In this paper, we develop a systematic approach grounded upon psychology and cinematography to address several important issues in affective understanding. An appropriate set of affective categories are identified and steps for their classification developed. A number of effective audiovisual cues are formulated to help bridge the affective gap. In particular, a holistic method of extracting affective information from the multifaceted audio stream has been introduced. Besides classifying every scene in Hollywood domain movies probabilistically into the affective categories, some exciting applications are demonstrated. The experimental results validate the proposed approach and the efficacy of the audiovisual cues.
  • Keywords
    cinematography; emotion recognition; feature extraction; image classification; psychology; video signal processing; cinematography; film; movie analysis; movie indexing; movie ranking; multifaceted audio stream; psychology; Bridges; Cinematography; Data mining; Event detection; Hidden Markov models; Indexing; Layout; Motion pictures; Psychology; Streaming media; Affective classification; audiovisual features; emotion; film grammar; movie scene; psychology;
  • fLanguage
    English
  • Journal_Title
    Circuits and Systems for Video Technology, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1051-8215
  • Type

    jour

  • DOI
    10.1109/TCSVT.2006.873781
  • Filename
    1637510