Dynamic probabilistic CCA for analysis of affective behaviour

Mihalis A. Nicolaou, Vladimir Pavlovic, Maja Pantic

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    18 Citations (Scopus)
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    Fusing multiple continuous expert annotations is a crucial problem in machine learning and computer vision, particularly when dealing with uncertain and subjective tasks related to affective behaviour. Inspired by the concept of inferring shared and individual latent spaces in probabilistic CCA (PCCA), we firstly propose a novel, generative model which discovers temporal dependencies on the shared/individual spaces (DPCCA). In order to accommodate for temporal lags which are prominent amongst continuous annotations, we further introduce a latent warping process. We show that the resulting model (DPCTW) (i) can be used as a unifying framework for solving the problems of temporal alignment and fusion of multiple annotations in time, and (ii) that by incorporating dynamics, modelling annotation/sequence specific biases, noise estimation and time warping, DPCTW outperforms state-of-the-art methods for both the aggregation of multiple, yet imperfect expert annotations as well as the alignment of affective behavior.
    Original languageUndefined
    Title of host publicationProceedings of the European Conference on Computer Vision, ECCV 2012
    Place of PublicationBerlin
    Number of pages14
    ISBN (Print)978-3-642-33785-7
    Publication statusPublished - 7 Oct 2012
    EventEuropean Conference on Computer Vision, ECCV 2012 - Florence, Italy
    Duration: 7 Oct 201213 Oct 2012

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Verlag
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    ConferenceEuropean Conference on Computer Vision, ECCV 2012
    Other7-13 October 2012


    • METIS-296256
    • IR-84313
    • EC Grant Agreement nr.: ERC-2007-STG-203143 (MAHNOB)
    • EC Grant Agreement nr.: FP7/288235
    • EC Grant Agreement nr.: FP7/2007-2013
    • EWI-22966

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