PittsburghV1, France, Analysis, bibRecord, 000177

Depression Estimation Using Audiovisual Features and Fisher Vector Encoding

Identifieur interne : 000177 ( France/Analysis ); précédent : 000176; suivant : 000178

Depression Estimation Using Audiovisual Features and Fisher Vector Encoding

Auteurs : Varun Jain [France] ; James L. Crowley [France] ; Anind Dey [États-Unis] ; Augustin Lux [France]

Source :

RBID : Hal:hal-01081358

English descriptors

mix :
- Dense Trajectories, Local Binary Patterns, Multimodal Af-fect Sensing.

Abstract

We investigate the use of two visual descriptors: Local Bi-nary Patterns-Three Orthogonal Planes(LBP-TOP) and Dense Trajectories for depression assessment on the AVEC 2014 challenge dataset. We encode the visual information gen-erated by the two descriptors using Fisher Vector encod-ing which has been shown to be one of the best performing methods to encode visual data for image classification. We also incorporate audio features in the final system to intro-duce multiple input modalities. The results produced using Linear Support Vector regression outperform the baseline method[16].

Url:

https://hal.inria.fr/hal-01081358

DOI: 10.1145/2661806.2661817

Affiliations:

Links toward previous steps (curation, corpus...)

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to stream Main, to step Merge: 000688
to stream Main, to step Curation: 000688
to stream Main, to step Exploration: 000688
to stream France, to step Extraction: 000177

Links to Exploration step

Hal:hal-01081358

Le document en format XML

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<orgName type="university">Université Grenoble-Alpes</orgName>
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<country>États-Unis</country>
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<author><name sortKey="Lux, Augustin" sort="Lux, Augustin" uniqKey="Lux A" first="Augustin" last="Lux">Augustin Lux</name>
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<desc><address><addrLine>CS 40700 - 38058 Grenoble cedex</addrLine>
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<country>France</country>
<placeName><settlement type="city">Grenoble</settlement>
<region type="region" nuts="2">Auvergne-Rhône-Alpes</region>
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</placeName>
<orgName type="university">Université Joseph Fourier</orgName>
<orgName type="institution" wicri:auto="newGroup">Université de Grenoble</orgName>
<placeName><settlement type="city">Grenoble</settlement>
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<idno type="DOI">10.1145/2661806.2661817</idno>
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<profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Dense Trajectories</term>
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<front><div type="abstract" xml:lang="en">We investigate the use of two visual descriptors: Local Bi-nary Patterns-Three Orthogonal Planes(LBP-TOP) and Dense Trajectories for depression assessment on the AVEC 2014 challenge dataset. We encode the visual information gen-erated by the two descriptors using Fisher Vector encod-ing which has been shown to be one of the best performing methods to encode visual data for image classification. We also incorporate audio features in the final system to intro-duce multiple input modalities. The results produced using Linear Support Vector regression outperform the baseline method[16].</div>
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<tree><country name="France"><region name="Auvergne-Rhône-Alpes"><name sortKey="Jain, Varun" sort="Jain, Varun" uniqKey="Jain V" first="Varun" last="Jain">Varun Jain</name>
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Depression Estimation Using Audiovisual Features and Fisher Vector Encoding

Depression Estimation Using Audiovisual Features and Fisher Vector Encoding

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