MediaEval 2015 - UMons at MediaEval 2015 Affective Impact of Movies Task including Violent Scenes Detection

Faculty of Engineering

Approach & Results

UMons at Affec-ve Impact of Movies Task

[email protected] Omar Sadda@, Emre Kulah, Gueorgui Pironkov

Université de Mons

!  Two subtasks !  Violent Scene Detec@on

!  Affec@ve impact of videos (arousal & valence)

!  High level of abstrac@on → difficult task

!  Dataset management !  Audio !  Video (frames)

Affec-ve Impact of Movies Task

2 Gueorgui Pironkov | TCTS Lab | MeadiEval 2015 -‐ Affec@ve Impact of Movies Task

Université de Mons

!  Feature extrac@on !  i-‐vector (Speaker iden@fica@on)

!  Classifica@on !  pLDA

Run 1


Université de Mons

Run 2


!  Feature extrac@on !  Dense op@cal flow maps

!  ConvNets

!  Classifica@on !  Fully connected MLP

Université de Mons

!  Using external data !  Feature extrac@on !  Training ConvNets on HMDB-‐51

ac@on recogni@on task

Run 3


!  Convolu@onal layers are used on the MediaEval dataset

!  Classifica@on !  Fully connected MLP

Université de Mons

!  Affec@ve impact evalua@on

Results


Run Valence (%) Arousal (%)

i-‐vector – pLDA 37,03 31,71

OFM – ConvNets 35,28 44,39

OFM – ConvNets – HMDB-‐51 37,28 52,44

Université de Mons

!  Violent scene detec@on

Results


Run MAP(%)

i-‐vector – pLDA 9,56

OFM – ConvNets 9,67

OFM – ConvNets – HMDB-‐51 6,56

Université de Mons

!  Audio features as promising as video features for valence classifica@on and violence detec@on

!  └> But not for arousal

!  HMDB feature extrac@on method inefficient for violence detec@on

!  └> But very efficient for arousal classifica@on

!  Future works: merging audio & video features

Conclusion


Education

MediaEval 2015 - UMons at MediaEval 2015 Affective Impact of Movies Task including Violent Scenes Detection