Dynamic probabilistic linear discriminant analysis for video classification

Conference paper


Fabris, A., Nicolaou, M., Kotsia, I. and Zafeiriou, S. 2017. Dynamic probabilistic linear discriminant analysis for video classification. ICASSP 2017: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). New Orleans, USA 05 - 09 Mar 2017 Institute of Electrical and Electronics Engineers (IEEE). pp. 2781-2785 https://doi.org/10.1109/ICASSP.2017.7952663
TypeConference paper
TitleDynamic probabilistic linear discriminant analysis for video classification
AuthorsFabris, A., Nicolaou, M., Kotsia, I. and Zafeiriou, S.
Abstract

Component Analysis (CA) comprises of statistical techniques that decompose signals into appropriate latent components, relevant to a task-at-hand (e.g., clustering, segmentation, classification). Recently, an explosion of research in CA has been witnessed, with several novel probabilistic models proposed (e.g., Probabilistic Principal CA, Probabilistic Linear Discriminant Analysis (PLDA), Probabilistic Canonical Correlation Analysis). PLDA is a popular generative probabilistic CA method, that incorporates knowledge regarding class-labels and furthermore introduces class-specific and sample-specific latent spaces. While PLDA has been shown to outperform several state-of-the-art methods, it is nevertheless a static model; any feature-level temporal dependencies that arise in the data are ignored. As has been repeatedly shown, appropriate modelling of temporal dynamics is crucial for the analysis of temporal data (e.g., videos). In this light, we propose the first, to the best of our knowledge, probabilistic LDA formulation that models dynamics, the so-called Dynamic-PLDA (DPLDA). DPLDA is a generative model suitable for video classification and is able to jointly model the label information (e.g., face identity, consistent over videos of the same subject), as well as dynamic variations of each individual video. Experiments on video classification tasks such as face and facial expression recognition show the efficacy of the proposed method

LanguageEnglish
ConferenceICASSP 2017: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Page range2781-2785
ISSN2379-190X
ISBN
Hardcover9781509041176
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Publication dates
Print05 Mar 2017
Online19 Jun 2017
Publication process dates
Deposited16 Jun 2017
Accepted06 Jan 2017
Output statusPublished
Accepted author manuscript
Copyright Statement

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Additional information

Published as: A. Fabris, M. A. Nicolaou, I. Kotsia and S. Zafeiriou, "Dynamic Probabilistic Linear Discriminant Analysis for video classification," 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, 2017, pp. 2781-2785.
doi: 10.1109/ICASSP.2017.7952663

Digital Object Identifier (DOI)https://doi.org/10.1109/ICASSP.2017.7952663
Book title2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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