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MediaEval 2017	Emotional Impact	of	Movies Task
Organizers: Emmanuel Dellandréa, Martijn Huigsloot, Liming Chen,
Yoann Baveye,Mats Sjöberg
Contact:	Emmanuel	Dellandréa – emmanuel.dellandrea@ec-lyon.fr
1MediaEval'17,13-15 September 2017,Dublin,Ireland
Task	overview
2MediaEval'17,13-15 September 2017,Dublin,Ireland
è Participants	are	expected	to	create	systems	that	
automatically	predict	the	emotional	impact	that	video	content	
will	have	on	viewers,	in	terms	of	valence,	arousal	and	fear
Context
An	evolution of	previous years tasks on	violence,	affect	and	
emotion	prediction from videos
Applications:
Personalized content	delivery
Movie recommendation
Video editing supervision
Video summarization
Protection	of	children from potential harmful content
3MediaEval'17,13-15 September 2017,Dublin,Ireland
Task description
Goal:	Deploy multimedia features and	models to	
automatically predict the	emotional impact	of	movies
Emotion	considered in	terms of	induced valence,	
arousal	and	fear
Long	movies	are	considered	and	the	emotional	impact	
has	to	be	predicted	for	consecutive	10-second	segments	
sliding	over	the	whole	movie	with	a	shift	of	5	seconds
Local	prediction	of	emotion
Should	allow	to	benefit	from	the	audio-visual	context	and	
temporal	dependencies
4MediaEval'17,13-15 September 2017,Dublin,Ireland
Task description
Two subtasks:
Valence/Arousal	prediction:	predict	a	score	of	expected	
valence	and	arousal	for	each	consecutive	10-second	
segments
Fear	prediction:	predict	for	each	consecutive	segments	
whether	they	are	likely	to	induce	fear	or	not
Targeted	use	case:	the	prediction	of	frightening	scenes	to	help	
systems	protecting	children	from	potentially	harmful	video
5MediaEval'17,13-15 September 2017,Dublin,Ireland
Run submissions and	evaluation
Up	to	5	runs for	each subtask
Models	can	rely	on	the	features	provided	by	the	
organizers	or	any	other	external	data
Standard	evaluation metrics:
Valence/arousal	prediction	subtask	(regression	
problem):	Mean	Square	Error,	Pearson’s	Correlation	
Coefficient
Fear	prediction	subtask	(binary	classification	problem):	
Accuracy,	Precision,	Recall	and	F1-score
6MediaEval'17,13-15 September 2017,Dublin,Ireland
Dataset:	LIRIS-ACCEDE
Development	set
30	movies selected among	160	movies	
under	Creative	Commons	licenses
Duration	between 117s	and	4,566s	(total	
duration:	~7	hours)
Continuous induced valence	and	arousal	
self-assessments
Test	set:
14	other	movies	selected	among	the	set	of	
160	movies	
Duration	between	210s	and	6,260s	(total	
duration:	~8	hours)
Audio	and	visual	features	provided
1582	general	purpose	audio	features
11	types	of	visual	features	(VGG16,	LBP,		
ACC,	Tamura,	...)	
7
LIRIS-ACCEDE available at:
http://liris-accede.ec-lyon.fr
MediaEval'17,13-15 September 2017,Dublin,Ireland
Ground	truth
Valence/arousal	predition	subtask:
Induced valence	and	arousal	self-assessments
16	annotators
ModifiedGtrace interface	and	joystick
è Arousal	and	valence	values	for	consecutive	10-second	
segments	sliding	over	the	whole	movie	with	a	shift	of	5	
seconds
8MediaEval'17,13-15 September 2017,Dublin,Ireland
Ground	truth
Fear	predition	subtask:
Use	of	tool	specifically	designed	for	the	classification	of	
audio-visual	media	(NICAM*)
Annotations	realized	by	two	well	experienced	team	
members	of	NICAM	trained	in	classification	of	media	
Each	movie	annotated	by	1	annotator	reporting	the	start	
and	stop	times	of	each	sequence	in	the	movie	expected	to	
induce	fear
è Segments	labeled	as	fear	(value	1)	if	they	intersect	one	of	
the	fear	sequences	and	as	not	fear	(value	0)	otherwise
9MediaEval'17,13-15 September 2017,Dublin,Ireland
* Netherlands Institute for the Classification of Audio-visual Media
Task participation
12	team	registered,	5	have	submitted runs
Grand	total	of	39	run submissions
Valence/arousal	prediction	subtask:
5 teams,	22	runs
Fear	prediction	subtask:
4	teams,	17	runs
10MediaEval'17,13-15 September 2017,Dublin,Ireland
Teams
MIC-TJU
Yun	Yi1,2,	Hanli	Wang2,	Jiangchuan	Wei2
1Gannan	Normal	University,	China
2Tongji	University,	China
HKBU	
Yang	Liu,	Zhonglei	Gu,	Tobey	H.	Ko
Hong	Kong	Baptist	University,	HKSAR,	China
THU-HCSI
Zitong	Jin,	Yuqi	Yao,	Ye	Ma,	Mingxing	Xu
Tsinghua	University,	China
11MediaEval'17,13-15 September 2017,Dublin,Ireland
Teams
BOUN-NKU
Nihan	Karslioglu1,	Yasemin	Timar1,	Albert	Ali	Salah1,	
Heysem	Kaya2
1Bogazici	University,	Turkey
2Namik	Kemal	University,	Turkey
TCNJ-CS
Sejong	Yoon
The	College	of	New	Jersey,	U.S.A
12MediaEval'17,13-15 September 2017,Dublin,Ireland
Participants’	approaches
Visual	Features
General	purpose	visual	features	provided	by	organizers
Auto	Color	Correlogram,	Color	and	Edge	Directivity	De-
scriptor,	Color	Layout,	Edge	Histogram,	Fuzzy	Color	and	
Texture	Histogram,	Gabor,	Joint	descriptor	joining	CEDD	
and	FCTH,	Scalable	Color,	Tamura,	Local	Binary	Patterns,	
fc6	layer	of	VGG16	network	
Motion	Keypoint	Trajectory	(MKT)	feature	
based	on	Histogram	of	Oriented	Gradient	(HOG),	Motion	
Boundary	Histogram	(MBH)	,	Histogram	of	Optical	Flow	
(HOF)	and	Trajectory-Based	Covariance	(TBC)
Two-stream	Convolutional	Networks	(ConvNets)
Dense	SIFT	features
13MediaEval'17,13-15 September 2017,Dublin,Ireland
Participants’	approaches
Audio	Features
Features	provided	by	organizers
1,582	audio	features	(EmoBase2010	from	OpenSmile)
Extended	Geneva	Minimalistic	Acoustic	Parameter	Set	
(eGeMAPS)
High-level	features
Lingering	features:	computationally	model	the	gradually	
amplifying	or	decaying	emotional	flow
14MediaEval'17,13-15 September 2017,Dublin,Ireland
Participants’	approaches
Feature	reduction
Principal	Components	Analysis
Fisher	Vectors
Biased	Discriminant	Embedding
15MediaEval'17,13-15 September 2017,Dublin,Ireland
Participants’	approaches
Regression/classification models
Support	Vector	Regression
Support	Vector	Classification
Multiple	Kernel	learning
Adaboost
Extreme	Learning	Machines
Random	forests
Long	Short-Term Memory	models
èApproaches	quite	similar	to	last	year
16MediaEval'17,13-15 September 2017,Dublin,Ireland
Valence/arousal	prediction	subtask
Valence
17MediaEval'17,13-15 September 2017,Dublin,Ireland
( Best Pearson's CC last year: 0.14 )
Valence/arousal	prediction	subtask
Arousal
18MediaEval'17,13-15 September 2017,Dublin,Ireland
( Best Pearson's CC last year: 0.23 )
Fear	prediction	subtask
Clarifying	the	evaluation	metrics:
True	Positives:	segments	are	predicted	as	fear	and	are	
actually	fear
True	Negatives:	segments	are	predicted	as	not	fear	and	
are	actually	not	fear
False	Positives:	segments	are	predicted	as	fear	and	are	
actually	not	fear
False	Negatives:	segments	are	predicted	as	not	fear	and	
are	actually	fear
MediaEval'17,13-15 September 2017,Dublin,Ireland 19
Fear	prediction	subtask
20MediaEval'17,13-15 September 2017,Dublin,Ireland
Accuracy = (TP+TN) / (TP+TN+FP+FN)
Fear	prediction	subtask
21MediaEval'17,13-15 September 2017,Dublin,Ireland
Precision = TP / (TP+FP)
Fear	prediction	subtask
22MediaEval'17,13-15 September 2017,Dublin,Ireland
Recall = TP / (TP+FN)
Fear	prediction	subtask
23MediaEval'17,13-15 September 2017,Dublin,Ireland
F1_score = 2.Precision.Recall / (Precision+Recall)
Conclusion
Participants’	approaches provided encouraging results,	
(better	than	last	year	for	valence/arousal	prediction)
Arousal	generally	better predicted than valence	
(consistent	with the	literature)
Some	submissions	rely	on	features/models	to	cope	with	
temporal	dependencies
Half	of	the	registered participants	have	submitted runs
è task too difficult ?
Both	subtasks	remain particularly challenging
High	subjectivity	of	emotions
Unbalanced	data	for	fear	prediction
24MediaEval'17,13-15 September 2017,Dublin,Ireland
The	future	of	the	Emotional	Impact	task
This	year development	and	test	sets	as	an	extension	
of	LIRIS-ACCEDE	dataset available at	http://liris-
accede.ec-lyon.fr
Some	possible	directions	of	investigation:
Collect	more	data	for	fear	prediction
Encourage	to	go	further	in	developping	approaches	to	
model	temporal	dependencies
Push	to	study	interplays	between	valence/arousal	and	
fear
A	novel orientation	of	the	task ?
25MediaEval'17,13-15 September 2017,Dublin,Ireland
Program	of	the	session
THUHCSI	in	MediaEval	2017	Emotional	Impact	of	Movies	Task
Presenter:	Mingxing	Xu,	Tsinghua	University,	China
MIC-TJU	in	MediaEval	2017	Emotional	Impact	of	Movies	Task
Presenter:	(Stand	in)	Emmanuel	Dellandréa,	Ecole	Centrale	
de	Lyon,	France
TCNJ-CS	@	MediaEval	2017	Emotional	Impact	of	Movie	Task	
(video)
BOUN-NKU	in	MediaEval	2017	Emotional	Impact	of	Movies	
Task	(video)
HKBU	at	MediaEval	2017	Emotional	Impact	of	Movies	Task	
(video)
MediaEval'17,13-15 September 2017,Dublin,Ireland 26
Thank	you	for	your	attention	!
MediaEval'17,13-15 September 2017,Dublin,Ireland 27

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