• DocumentCode
    3425832
  • Title

    How Related Exemplars Help Complex Event Detection in Web Videos?

  • Author

    Yi Yang ; Zhigang Ma ; Zhongwen Xu ; Shuicheng Yan ; Hauptmann, Alexander G.

  • Author_Institution
    Sch. of Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, USA
  • fYear
    2013
  • fDate
    1-8 Dec. 2013
  • Firstpage
    2104
  • Lastpage
    2111
  • Abstract
    Compared to visual concepts such as actions, scenes and objects, complex event is a higher level abstraction of longer video sequences. For example, a "marriage proposal" event is described by multiple objects (e.g., ring, faces), scenes (e.g., in a restaurant, outdoor) and actions (e.g., kneeling down). The positive exemplars which exactly convey the precise semantic of an event are hard to obtain. It would be beneficial to utilize the related exemplars for complex event detection. However, the semantic correlations between related exemplars and the target event vary substantially as relatedness assessment is subjective. Two related exemplars can be about completely different events, e.g., in the TRECVID MED dataset, both bicycle riding and equestrianism are labeled as related to "attempting a bike trick" event. To tackle the subjectiveness of human assessment, our algorithm automatically evaluates how positive the related exemplars are for the detection of an event and uses them on an exemplar-specific basis. Experiments demonstrate that our algorithm is able to utilize related exemplars adaptively, and the algorithm gains good performance for complex event detection.
  • Keywords
    image sequences; video signal processing; TRECVID MED dataset; bicycle riding; bike trick event; complex event detection; equestrianism; human assessment; marriage proposal; positive exemplars; semantic correlations; video sequences; web videos; Event detection; Feature extraction; Proposals; Support vector machines; Video sequences; Videos; Visualization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Vision (ICCV), 2013 IEEE International Conference on
  • Conference_Location
    Sydney, NSW
  • ISSN
    1550-5499
  • Type

    conf

  • DOI
    10.1109/ICCV.2013.456
  • Filename
    6751372