The detection of concept frames using Clustering Multi-Instance Learning

D.M.J. Tax, E. Hendriks, Maja Pantic, M.F. Valstar

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    17 Citations (Scopus)
    76 Downloads (Pure)

    Abstract

    The classification of sequences requires the combination of information from different time points. In this paper the detection of facial expressions is considered. Experiments on the detection of certain facial muscle activations in videos show that it is not always required to model the sequences fully, but that the presence of specific frames (the concept frame) can be sufficient for a reliable detection of certain facial expression classes. For the detection of these concept frames a standard classifier is often sufficient, although a more advanced clustering approach performs better in some cases.
    Original languageUndefined
    Title of host publication20th International Conference on Pattern Recognition, ICPR 2010
    Place of PublicationUSA
    PublisherIEEE Computer Society
    Pages2917-2920
    Number of pages4
    ISBN (Print)978-1-4244-7542-1
    DOIs
    Publication statusPublished - 26 Aug 2010
    Event20th International Conference on Pattern Recognition 2010 - Istanbul Convention & Exhibition Centre, Istanbul, Turkey
    Duration: 23 Aug 201026 Aug 2010
    Conference number: 20
    https://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=16097

    Publication series

    Name
    PublisherIEEE Computer Society
    ISSN (Print)1051-4651

    Conference

    Conference20th International Conference on Pattern Recognition 2010
    Abbreviated titleICPR 2010
    CountryTurkey
    CityIstanbul
    Period23/08/1026/08/10
    Internet address

    Keywords

    • EC Grant Agreement nr.: FP7/231287
    • EWI-19543
    • IR-75974
    • HMI-MI: MULTIMODAL INTERACTIONS
    • EC Grant Agreement nr.: FP7/211486
    • METIS-276357
    • time series classification
    • multi-instance learning
    • Classification

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