This paper presents the approach used by the RECOD team to address the challenges provided in the MediaEval 2015 Affective Impact of Movies Task. We designed various video classifiers, which relied on bags of visual features, and on bags of auditory features. We combined these classifiers using different approaches, ranging from majority voting to machine-learned techniques on the training dataset. We only participated in the Violence Detection subtask. http://ceur-ws.org/Vol-1436/ http://www.multimediaeval.org

1. REC D at MediaEval 2015:
Affective Impact of Movies Task
Daniel Moreira, Sandra Avila, Mauricio
Perez, Daniel Moraes, Vanessa Testoni,
Eduardo Valle, Siome Goldenstein,
Anderson Rocha
daniel.moreira@ic.unicamp.br

2. Team
• 3 professors from Unicamp;
• 1 postdoctoral researcher;
• 1 researcher from Samsung;
• 1 PhD candidate;
• 1 MSc candidate;
• 1 developer.

3. Context
• Unicamp – Samsung partnership;
• Project: Sensitive Media Analysis;
• Ongoing patent request
–We cannot disclose some technical
details.

4. MediaEval 2015
• Affective Impact of Movies Task
–Induced Affect Detection subtask;
–Violence Detection subtask.

5. Solution Description
• 5 classifiers
–2 based on bags of visual features;
–3 based on bags of auditory features.
• 2 types of late fusion
–Majority voting;
–Machine-learned on the classification
scores of the training dataset.

6. Bags of Visual Features
Video
resolution
reduction
SURF
STIP-like
descriptor
PCA
Bags of
features
Classification

7. Bags of Auditory Features
Speech
Music
Tone
PCA
Bags of
features
Classification

8. External Data
• 2015 dataset
–6,144 video clips;
–272 are violent.
• External source
–ME VSD 2014 web videos;
• Sequences of 10s to 12s from the violent
scenes;
• 252 additional segments.

9. Submitted Runs
0.0960NoNoSTIP-likeYes5
0.0924NoToneNoYes4
0.1126MVAllAllYes3
0.0690RFAllAllNo2
0.1143MVAllAllNo1
MAPFusionAuditoryVisualExternal

10. Results and Discussion
0.0960NoNoSTIP-likeYes5
0.0924NoToneNoYes4
0.1126MVAllAllYes3
0.0690RFAllAllNo2
0.1143MVAllAllNo1
MAPFusionAuditoryVisualExternal

11. Results and Discussion
0.0960NoNoSTIP-likeYes5
0.0924NoToneNoYes4
0.1126MVAllAllYes3
0.0690RFAllAllNo2
0.1143MVAllAllNo1
MAPFusionAuditoryVisualExternal

12. Results and Discussion
0.0960NoNoSTIP-likeYes5
0.0924NoToneNoYes4
0.1126MVAllAllYes3
0.0690RFAllAllNo2
0.1143MVAllAllNo1
MAPFusionAuditoryVisualExternal

13. Results and Discussion
0.0960NoNoSTIP-likeYes5
0.0924NoToneNoYes4
0.1126MVAllAllYes3
0.0690RFAllAllNo2
0.1143MVAllAllNo1
MAPFusionAuditoryVisualExternal

14. Results and Discussion
• We failed at augmenting the
training dataset.
• Majority Voting Fusion improved
results.

15. Results and Discussion
• We did not have enough positive
samples to learn a better
classifier.
• How to use textual features
(specially subtitles)?

16. Acknowledgments
• MediaEval 2015 staff and
organizers.
• Sansumg, CNPq, FAPESP, and
CAPES.

17. Thank you!