Learning slow features for behavior analysis

Lazaros Zafeiriou, Mihalis A. Nicolaou, Stefanos Zafeiriou, Symeon Nikitids, Maja Pantic

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    19 Citations (Scopus)
    26 Downloads (Pure)

    Abstract

    A recently introduced latent feature learning technique for time varying dynamic phenomena analysis is the socalled Slow Feature Analysis (SFA). SFA is a deterministic component analysis technique for multi-dimensional sequences that by minimizing the variance of the first order time derivative approximation of the input signal finds uncorrelated projections that extract slowly-varying features ordered by their temporal consistency and constancy. In this paper, we propose a number of extensions in both the deterministic and the probabilistic SFA optimization frameworks. In articular, we derive a novel deterministic SFA algorithm that is able to identify linear projections that extract the common slowest varying features of two or more sequences. In addition, we propose an Expectation Maximization (EM) algorithm to perform inference in a probabilistic ormulation of SFA and similarly extend it in order to handle two and more time varying data sequences. Moreover, we demonstrate that the probabilistic SFA (EMSFA) algorithm that discovers the common slowest varying latent space of multiple sequences can be combined with dynamic time warping chniques for robust sequence timealignment. The proposed SFA algorithms were applied for facial behavior analysis demonstrating their usefulness and appropriateness for this task.
    Original languageUndefined
    Title of host publicationProceedings of the IEEE International Conference on Computer Vision, ICCV 2013
    Place of PublicationUSA
    PublisherIEEE Computer Society
    Pages2840-2847
    Number of pages8
    ISBN (Print)1550-5499
    DOIs
    Publication statusPublished - 1 Dec 2013
    EventIEEE International Conference on Computer Vision 2013 - Sydney Conference Centre, Sydney, Australia
    Duration: 1 Dec 20138 Dec 2013
    http://www.pamitc.org/iccv13/

    Publication series

    NameIEEE International Conference on Computer Vision
    PublisherIEEE Computer Society
    ISSN (Print)1550-5499

    Conference

    ConferenceIEEE International Conference on Computer Vision 2013
    Abbreviated titleICCV 2013
    CountryAustralia
    CitySydney
    Period1/12/138/12/13
    Internet address

    Keywords

    • HMI-HF: Human Factors
    • EC Grant Agreement nr.: FP7/288235
    • METIS-302865
    • EWI-24261
    • IR-89697
    • EC Grant Agreement nr.: FP7/2007-2013

    Cite this

    Zafeiriou, L., Nicolaou, M. A., Zafeiriou, S., Nikitids, S., & Pantic, M. (2013). Learning slow features for behavior analysis. In Proceedings of the IEEE International Conference on Computer Vision, ICCV 2013 (pp. 2840-2847). (IEEE International Conference on Computer Vision). USA: IEEE Computer Society. https://doi.org/10.1109/ICCV.2013.353
    Zafeiriou, Lazaros ; Nicolaou, Mihalis A. ; Zafeiriou, Stefanos ; Nikitids, Symeon ; Pantic, Maja. / Learning slow features for behavior analysis. Proceedings of the IEEE International Conference on Computer Vision, ICCV 2013. USA : IEEE Computer Society, 2013. pp. 2840-2847 (IEEE International Conference on Computer Vision).
    @inproceedings{2915458ccdfd457e94f77be560f1acbf,
    title = "Learning slow features for behavior analysis",
    abstract = "A recently introduced latent feature learning technique for time varying dynamic phenomena analysis is the socalled Slow Feature Analysis (SFA). SFA is a deterministic component analysis technique for multi-dimensional sequences that by minimizing the variance of the first order time derivative approximation of the input signal finds uncorrelated projections that extract slowly-varying features ordered by their temporal consistency and constancy. In this paper, we propose a number of extensions in both the deterministic and the probabilistic SFA optimization frameworks. In articular, we derive a novel deterministic SFA algorithm that is able to identify linear projections that extract the common slowest varying features of two or more sequences. In addition, we propose an Expectation Maximization (EM) algorithm to perform inference in a probabilistic ormulation of SFA and similarly extend it in order to handle two and more time varying data sequences. Moreover, we demonstrate that the probabilistic SFA (EMSFA) algorithm that discovers the common slowest varying latent space of multiple sequences can be combined with dynamic time warping chniques for robust sequence timealignment. The proposed SFA algorithms were applied for facial behavior analysis demonstrating their usefulness and appropriateness for this task.",
    keywords = "HMI-HF: Human Factors, EC Grant Agreement nr.: FP7/288235, METIS-302865, EWI-24261, IR-89697, EC Grant Agreement nr.: FP7/2007-2013",
    author = "Lazaros Zafeiriou and Nicolaou, {Mihalis A.} and Stefanos Zafeiriou and Symeon Nikitids and Maja Pantic",
    note = "eemcs-eprint-24261",
    year = "2013",
    month = "12",
    day = "1",
    doi = "10.1109/ICCV.2013.353",
    language = "Undefined",
    isbn = "1550-5499",
    series = "IEEE International Conference on Computer Vision",
    publisher = "IEEE Computer Society",
    pages = "2840--2847",
    booktitle = "Proceedings of the IEEE International Conference on Computer Vision, ICCV 2013",
    address = "United States",

    }

    Zafeiriou, L, Nicolaou, MA, Zafeiriou, S, Nikitids, S & Pantic, M 2013, Learning slow features for behavior analysis. in Proceedings of the IEEE International Conference on Computer Vision, ICCV 2013. IEEE International Conference on Computer Vision, IEEE Computer Society, USA, pp. 2840-2847, IEEE International Conference on Computer Vision 2013, Sydney, Australia, 1/12/13. https://doi.org/10.1109/ICCV.2013.353

    Learning slow features for behavior analysis. / Zafeiriou, Lazaros; Nicolaou, Mihalis A.; Zafeiriou, Stefanos; Nikitids, Symeon; Pantic, Maja.

    Proceedings of the IEEE International Conference on Computer Vision, ICCV 2013. USA : IEEE Computer Society, 2013. p. 2840-2847 (IEEE International Conference on Computer Vision).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    TY - GEN

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    AU - Nicolaou, Mihalis A.

    AU - Zafeiriou, Stefanos

    AU - Nikitids, Symeon

    AU - Pantic, Maja

    N1 - eemcs-eprint-24261

    PY - 2013/12/1

    Y1 - 2013/12/1

    N2 - A recently introduced latent feature learning technique for time varying dynamic phenomena analysis is the socalled Slow Feature Analysis (SFA). SFA is a deterministic component analysis technique for multi-dimensional sequences that by minimizing the variance of the first order time derivative approximation of the input signal finds uncorrelated projections that extract slowly-varying features ordered by their temporal consistency and constancy. In this paper, we propose a number of extensions in both the deterministic and the probabilistic SFA optimization frameworks. In articular, we derive a novel deterministic SFA algorithm that is able to identify linear projections that extract the common slowest varying features of two or more sequences. In addition, we propose an Expectation Maximization (EM) algorithm to perform inference in a probabilistic ormulation of SFA and similarly extend it in order to handle two and more time varying data sequences. Moreover, we demonstrate that the probabilistic SFA (EMSFA) algorithm that discovers the common slowest varying latent space of multiple sequences can be combined with dynamic time warping chniques for robust sequence timealignment. The proposed SFA algorithms were applied for facial behavior analysis demonstrating their usefulness and appropriateness for this task.

    AB - A recently introduced latent feature learning technique for time varying dynamic phenomena analysis is the socalled Slow Feature Analysis (SFA). SFA is a deterministic component analysis technique for multi-dimensional sequences that by minimizing the variance of the first order time derivative approximation of the input signal finds uncorrelated projections that extract slowly-varying features ordered by their temporal consistency and constancy. In this paper, we propose a number of extensions in both the deterministic and the probabilistic SFA optimization frameworks. In articular, we derive a novel deterministic SFA algorithm that is able to identify linear projections that extract the common slowest varying features of two or more sequences. In addition, we propose an Expectation Maximization (EM) algorithm to perform inference in a probabilistic ormulation of SFA and similarly extend it in order to handle two and more time varying data sequences. Moreover, we demonstrate that the probabilistic SFA (EMSFA) algorithm that discovers the common slowest varying latent space of multiple sequences can be combined with dynamic time warping chniques for robust sequence timealignment. The proposed SFA algorithms were applied for facial behavior analysis demonstrating their usefulness and appropriateness for this task.

    KW - HMI-HF: Human Factors

    KW - EC Grant Agreement nr.: FP7/288235

    KW - METIS-302865

    KW - EWI-24261

    KW - IR-89697

    KW - EC Grant Agreement nr.: FP7/2007-2013

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    DO - 10.1109/ICCV.2013.353

    M3 - Conference contribution

    SN - 1550-5499

    T3 - IEEE International Conference on Computer Vision

    SP - 2840

    EP - 2847

    BT - Proceedings of the IEEE International Conference on Computer Vision, ICCV 2013

    PB - IEEE Computer Society

    CY - USA

    ER -

    Zafeiriou L, Nicolaou MA, Zafeiriou S, Nikitids S, Pantic M. Learning slow features for behavior analysis. In Proceedings of the IEEE International Conference on Computer Vision, ICCV 2013. USA: IEEE Computer Society. 2013. p. 2840-2847. (IEEE International Conference on Computer Vision). https://doi.org/10.1109/ICCV.2013.353