In contrast to existing datasets with very few video resources and limited accessibility due to copyright constraints, LIRIS-ACCEDE consists of videos with a large content diversity annotated along affective dimensions. All excerpts are shared under Creative Commons licenses and can thus be freely distributed without copyright issues. The dataset (the video clips, annotations, features and protocols) are publicly available.

LIRIS-ACCEDE is composed of six collections:

  • Discrete LIRIS-ACCEDE - Induced valence and arousal rankings for 9800 short video excerpts extracted from 160 movies. Estimated affective scores are also available.
  • Continuous LIRIS-ACCEDE - Continuous induced valence and arousal self-assessments for 30 movies. Raw and post-processed GSR measurements are also available.
  • MediaEval 2015 affective impact of movies task downloads - Violence annotations and affective classes for the 9800 excerpts of the discrete LIRIS-ACCEDE part, plus for additional 1100 excerpts used to extend the test set for the MediaEval 2015 affective impact of movies task.
  • MediaEval 2016 Emotional Impact of Movies task downloads - Test set for the MediaEval 2016 Emotional Impact of Movies task: 1200 additional videos excerpts for the Global Annotation subtask and 10 additional movies for the Continous Annotation subtask.
  • MediaEval 2017 Emotional Impact of Movies task downloads - Valence/arousal and fear annotations for the development and test sets of the MediaEval 2017 Emotional Impact of Movies Task. Visual and audio features are also provided.
  • MediaEval 2018 Emotional Impact of Movies task downloads - Valence/arousal and fear annotations for the development and test sets of the MediaEval 2018 Emotional Impact of Movies Task. Visual and audio features are also provided.

 

A general presentation of the LIRIS-ACCEDE dataset is available here :

 

A complete description of the discrete collection of the dataset can be found in the following journal paper:

  • Y. Baveye, E. Dellandrea, C. Chamaret, and L. Chen, “LIRIS-ACCEDE: A Video Database for Affective Content Analysis,” in IEEE Transactions on Affective Computing, 2015[PDF]

 

Continuous annotations are described in the following publication:

  • Y. Baveye, E. Dellandrea, C. Chamaret, and L. Chen, “Deep Learning vs. Kernel Methods: Performance for Emotion Prediction in Videos,” in 2015 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), 2015. [PDF]

 

The collection for the MediaEval 2015 Affective Impact of Movies task is introduced in the following publication:

  • M. Sjöberg, Y. Baveye, H. Wang, V. L. Quang, B. Ionescu, E. Dellandréa, M. Schedl, C.-H. Demarty, and L. Chen, “The mediaeval 2015 affective impact of movies task,” in MediaEval 2015 Workshop, 2015. [PDF]

 

The collection for the MediaEval 2016 Emotional Impact of Movies task is introduced in the following publication:

  • E. Dellandrea, L. Chen, Y. Baveye, M. Sjoberg and C. Chamaret, "The MediaEval 2016 Emotional Impact of Movies Task", in Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, The Netherlands, October 20-21, 2016. [PDF]

 

The collection for the MediaEval 2017 Emotional Impact of Movies task is introduced in the following publication:

  • E. Dellandrea, Martijn Huigsloot, L. Chen, Y. Baveye and M. Sjoberg, "The MediaEval 2017 Emotional Impact of Movies Task", in Working Notes Proceedings of the MediaEval 2017 Workshop, Dublin, Ireland, September 13-15, 2017. [PDF]

 

The collection for the MediaEval 2018 Emotional Impact of Movies task is introduced in the following publication:

  • E. Dellandréa, M. Huigsloot, L. Chen, Y. Baveye, Z. Xiao and M. Sjöberg, "The MediaEval 2018 Emotional Impact of Movies Task", in Working Notes Proceedings of the MediaEval 2018 Workshop, Sophia Antipolis, France, October 29-31, 2018. [PDF]

 

Database content

LIRIS-ACCEDE is the Annotated Creative Commons Emotional DatabasE. It is composed of six collections:

  • Discrete LIRIS-ACCEDE - Induced valence and arousal rankings for 9800 short video excerpts extracted from 160 movies. Estimated affective scores are also available.
  • Continuous LIRIS-ACCEDE - Continuous induced valence and arousal self-assessments for 30 movies. Raw and post-processed GSR measurements are also available.
  • MediaEval 2015 Affective Impact of Movies collection - Violence annotations and affective classes for the 9800 excerpts of the discrete LIRIS-ACCEDE part, plus for additional 1100 excerpts used to extend the test set for the MediaEval 2015 affective impact of movies task.
  • MediaEval 2016 Emotional Impact of Movies collection - Test set for the MediaEval 2016 Emotional Impact of Movies task: 1200 additional videos excerpts for the Global Annotation subtask and 10 additional movies for the Continous Annotation subtask.
  • MediaEval 2017 Emotional Impact of Movies collection - Valence/arousal and fear annotations for the development and test sets of the MediaEval 2017 Emotional Impact of Movies Task. Visual and audio features are also provided.
  • MediaEval 2018 Emotional Impact of Movies collection - Valence/arousal and fear annotations for the development and test sets of the MediaEval 2018 Emotional Impact of Movies Task. Visual and audio features are also provided.

 

The database is composed only of movies and excerpts from movies shared under Creative Commons licenses. The Creative Commons licenses allow creators to use standardized way to give the public permission to share and use their creative work under certain conditions of their choice. Creative Commons licenses consist of four major condition modules: Attribution (BY), requiring attribution to the original author; Share Alike (SA), allowing derivative works under the same or a similar license; Non-Commercial (NC), preventing the work from being used for commercial purposes; and No Derivative Works (ND), allowing only the use of the original work without any modification. Moreover, movies shared under Creative Commons licenses are often little known films, which limits viewers’ prior memories. The movies selected to be part of the LIRIS-ACCEDE are shared under Creative Commons licenses with BY, SA or NC modules. Movies shared with ND module are not taken into account in this work since it is not allowed to modify these videos, and therefore it is not allowed to segment them. Thus, using videos shared under Creative Commons licenses allows us to share the database publicly.

Discrete LIRIS-ACCEDE collection

160 films and short films with different genres are used and segmented into 9800 video clips. The total time of all 160 films is 73 hours 41 minutes and 7 seconds, and a video clip is extracted on average every 27s. The 9800 segmented video clips last between 8 and 12 seconds and are representative enough to conduct experiments. Indeed, the length of extracted segments is large enough to get consistent excerpts allowing the viewer to feel emotions and is also small enough to make the viewer feel only one emotion per excerpt. The content of the movie is also considered to create homogeneous, consistent and meaningful excerpts not to disturb the viewers. A robust shot and fade in/out detection has been implemented using to make sure that each extracted video clip start and end with a shot or a fade. Furthermore, the order of excerpts within a film is kept, allowing to study the temporal transitions of emotions. Several movie genres are represented in this collection of movies such as horror, comedy, drama, action and so on. Languages are mainly English with a small set of Italian, Spanish, French and others subtitled in English.

In order to sort the database along the induced valence and arousal axis, pairs of video clips were presented to annotators on Crowdflower. CrowdFlower is a crowdsourcing service which has over 50 labor channel partners, among them Amazon Mechanical Turk and TrialPay CrowdFlower differs from these individual networks because they offer enterprise solutions and a higher degree of quality control, called “Gold Standard Data”, to ensure the accuracy on the tasks.

Related publications:

  • Y. Baveye, E. Dellandrea, C. Chamaret, and L. Chen, “LIRIS-ACCEDE: A Video Database for Affective Content Analysis,” in IEEE Transactions on Affective Computing, 2015[PDF]
  • Y. Baveye, J.-N. Bettinelli, E. Dellandrea, L. Chen, and C. Chamaret, “A Large Video Database for Computational Models of Induced Emotion,” in 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), 2013, pp. 13–18. [PDF]
  • Y. Baveye, E. Dellandrea, C. Chamaret, and L. Chen, “From crowdsourced rankings to affective ratings,” in IEEE International Conference on Multimedia and Expo Workshops (ICMEW), Jul. 2014, pp. 1–6. [PDF]
  • Y. Baveye, C. Chamaret, E. Dellandrea, and L. Chen, “A protocol for cross-validating large crowdsourced data: The case of the LIRIS-ACCEDE affective video dataset,” in Proceedings of the 2014 International ACM Workshop on Crowdsourcing for Multimedia, ser. CrowdMM ’14, 2014, pp. 3–8. [PDF]

 

Continuous LIRIS-ACCEDE collection

The aim of this new experiment is to collect continuous annotations on whole movies. To select the movies to be annotated, we simply looked at the movies included in the LIRIS-ACCEDE dataset since they all share the desirable property to be shared under Creative Commons licenses and can thus be freely used and distributed without copyright issues as long as the original creator is credited. The total length of the selected movies was the only constraint. It had to be smaller than eight hours to create an experiment of acceptable duration. The selection process ended with the choice of 30 movies so that their genre, content, language and duration are diverse enough to be representative of the original LIRIS-ACCEDE dataset.

The annotation process aimed at continuously collecting the self-assessments of arousal and valence that viewers feel while watching the movies. To collect continuous annotations, we have used a modified version of the GTrace program originally developed by Cowie et al.. Annotations were collected from ten French paid participants (seven female and three male) ranging in age from 18 to 27 years (mean=21.9 +- 2.5 SD). Participants had different educational backgrounds, from undergraduate students to recently graduated master students.

Related publications:

  • Y. Baveye, E. Dellandrea, C. Chamaret, and L. Chen, “Deep Learning vs. Kernel Methods: Performance for Emotion Prediction in Videos,” in 2015 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), 2015. [PDF]
  • T. Li, Y. Baveye, C. Chamaret, E. Dellandrea, and L. Chen, “Continuous Arousal Self-assessments Validation Using Real-time Physiological Responses,” in ASM (ACM MM workshop), 2015. [PDF]

 

MediaEval 2015 Affective Impact of Movies collection

The Affective Impact of Movies Task is part of the MediaEval 2015 Benchmarking Initiative. The overall use case scenario of the task is to design a video search system that uses automatic tools to help users find videos that fit their particular mood, age or preferences. To address this, we present two subtasks:

  • Induced affect detection: the emotional impact of a video or movie can be a strong indicator for search or recommendation;
  • Violence detection: detecting violent content is an important aspect of filtering video content based on age.

 

This year a single data set is proposed: 10,900 short video clips extracted from 199 Creative Commons-licensed movies of various genres. The movies are split into a development set – intended for training and validation – and a test set as 100, respectively 99 movies, resulting in 6,144 respectively 4,756 extracted short video clips. For each of the 10,900 video clips, the ground truth consists of: a binary value to indicate the presence of violence, the class of the excerpt for felt arousal (calm-neutral-active), and the class for felt valence (negative-neutral-positive).

Related publication:

  • M. Sjöberg, Y. Baveye, H. Wang, V. L. Quang, B. Ionescu, E. Dellandréa, M. Schedl, C.-H. Demarty, and L. Chen, “The mediaeval 2015 affective impact of movies task,” in MediaEval 2015 Workshop, 2015. [PDF]

 

MediaEval 2016 Emotional Impact of Movies collection

The Emotional Impact of Movies Task is part of the MediaEval 2016 Benchmarking Initiative. The task requires participants to deploy multimedia features to automatically predict the emotional impact of movies. We are focusing on felt emotion, i.e., the actual emotion of the viewer when watching the video, rather than for example what the viewer believes that he or she is expected to feel. The emotion is considered in terms of valence and arousal. Valence is defined as a continuous scale from most negative to most positive emotions, while arousal is defined continuously from calmest to most active emotions. Two subtasks are considered:

  • Global emotion prediction: given a short video clip (around 10 seconds), participants’ systems are expected to predict a score of induced valence (negative-positive) and induced arousal (calm-excited) for the whole clip;
  • Continuous emotion prediction: as an emotion felt during a scene may be influenced by the emotions felt during the previous ones, the purpose here is to consider longer videos, and to predict the valence and arousal continuously along the video. Thus, a score of induced valence and arousal should be provided for each 1s-segment of the video.

 

The development set is composed of the Discrete LIRIS-ACCEDE part for the first subtask, and the Continuous LIRIS-ACCEDE part for the second subtask. In addition to the development set, a test set is also provided to assess participants’ methods performance. 49 new movies under Creative Commons licenses have been considered. With the same protocol as the one used for the development set, 1,200 additional short video clips have been extracted for the first subtask (between 8 and 12 seconds), and 10 long movies (from 25 minutes to 1 hour and 35 minutes) have been selected for the second subtask (for a total duration of 11.48 hours).

Related publication:

  • E. Dellandrea, L. Chen, Y. Baveye, M. Sjoberg and C. Chamaret, "The MediaEval 2016 Emotional Impact of Movies Task", in Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, The Netherlands, October 20-21, 2016. [PDF]

 

MediaEval 2017 Emotional Impact of Movies collection

The Emotional Impact of Movies Task is part of the MediaEval 2017 Benchmarking Initiative. The task requires participants to deploy multimedia features to automatically predict the emotional impact of movies. We are focusing on felt emotion, i.e., the actual emotion of the viewer when watching the video, rather than for example what the viewer believes that he or she is expected to feel. The emotion is considered in terms of valence, arousal and fear. Two new scenarios are proposed as subtasks. In both cases, 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:

  • Valence/Arousal prediction: participants’ systems are supposed to predict a score of expected valence and arousal for each consecutive 10-second segments. Valence is defined as a continuous scale from most negative to most positive emotions, while arousal is defined continuously from calmest to most active emotions;
  • Fear prediction: the purpose here is to predict for each consecutive 10-second segments whether they are likely to induce fear or not. The targeted use case is the prediction of frightening scenes to help systems protecting children from potentially harmful video content. This subtask is complementary to the valence/arousal prediction task in the sense that the mapping of discrete emotions into the 2D valence/arousal space is often overlapped (for instance, fear, disgust and anger are overlapped since they are characterized with very negative valence and high arousal).

 

The continuous part of LIRIS-ACCEDE is used as the development test for both subtasks. The test set consists of a selection of 14 movies other than the selection of the 160 original movies. They are between 210 and 6,260 seconds long. The total length of the 14 selected movies is 7 hours, 57 minutes and 13 seconds. The ground truth consists, for each 10-second segment, of a valence value, an arousal value and a binary value to indicate if the segment is supposed to induce fear or not.

Related publication:

  • E. Dellandrea, Martijn Huigsloot, L. Chen, Y. Baveye and M. Sjoberg, "The MediaEval 2017 Emotional Impact of Movies Task", in Working Notes Proceedings of the MediaEval 2017 Workshop, Dublin, Ireland, September 13-15, 2017. [PDF]

 

MediaEval 2018 Emotional Impact of Movies collection

The Emotional Impact of Movies Task is part of the MediaEval 2018 Benchmarking Initiative. The task requires participants to deploy multimedia features to automatically predict the emotional impact of movies. We are focusing on felt emotion, i.e., the actual emotion of the viewer when watching the video, rather than for example what the viewer believes that he or she is expected to feel. The emotion is considered in terms of valence, arousal and fear. Two new scenarios are proposed as subtasks. In both cases, long movies are considered:

  • Valence/Arousal prediction: participants’ systems have to predict a score of expected valence and arousal continuously (every second) along movies. Valence is defined as a continuous scale from most negative to most positive emotions, while arousal is defined continuously from calmest to most active emotions;
  • Fear prediction: the purpose here is to predict beginning and ending times of sequences inducing fear in movies. The targeted use case is the prediction of frightening scenes to help systems protecting children from potentially harmful video content.

 

A total of 44 movies (total duration of 15 hours and 20 minutes) selected from the set of 160 movies of the LIRIS-ACCEDE dataset are provided as development set for both subtasks with the annotations according to fear, valence and arousal. A complementary set of 10 movies (11 hours and 29 minutes) is available for the first subtask with the valence and arousal annotations. The test set consists of 12 other movies selected from the set of 160 movies, for a total duration of 8 hours and 56 minutes. In addition to the video data, participants are also provided with general purpose audio and visual content features. The ground truth consists of valence and arousal values for each second of each movie (first subtask) and the beginning and ending times of each sequence inducing fear in each movie (second subtask).

Related publication:

  • E. Dellandrea, M. Huigsloot, L. Chen, Y. Baveye, Z. Xiao and M. Sjoberg, "The MediaEval 2018 Emotional Impact of Movies Task", in Working Notes Proceedings of the MediaEval 2018 Workshop, Sophia Antipolis, France, October 29-31, 2018. [PDF]

 

List of movies

NameCreditsLengthLicence
20 Mississippi Barnett Brettler 00:58:38 20 Mississippi shared under Creative Commons Attribution-NonCommercial 3.0 Unported license at http://vimeo.com/20043857
21 Below Robbie Stauder 01:31:17 21 Below shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/38939998
52 Films/52 Weeks Renee Ronceros, Samantha Simmonds & Javier Ronceros 03:01:55 52 Films/52 Weeks: a year of filmmaking shared under Creative Commons Public Domain 3.0 license at http://www.52films52weeks.com/52films52weeks/Welcome.html
After The Rain Hits Enterprises & Video 00:09:49 After The Rain shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vimeo.com/40104084
Attitude Matters Marco Luca & Laura Aloi 00:22:52 Attitude Matters shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/17778716
Barely Legal Stories Jonathan Musset 00:16:28 Barely Legal Stories shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vimeo.com/30344973
Best Nice Monster 00:07:56 Best shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/44163644
Between Viewings Raphael Biss 00:14:46 Between Viewings shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/43763789
Big Buck Bunny Sacha Goedegebure 00:09:56 Big Buck Bunny shared under Creative Commons Attribution 3.0 license at http://www.bigbuckbunny.org/
Boiling Point Jack Leigh 00:14:04 Boiling Point shared under Creative Commons Attribution-ShareAlike 3.0 Unported license at http://vimeo.com/24169479
Burgundies Boys Steve Galley 01:18:43 Burgundies Boys shared under Creative Commons Attribution-NonCommercial 3.0 Unported license at http://vodo.net/boys
California Dreaming Bregtje van der Haak 00:49:13 California Dreaming shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vodo.net/californiadreaming
Capitalism Communism: Is this love? FILSPRODUCTION 00:07:27 Capitalism Communism Is this love? shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vimeo.com/53307142
Chatter Leo Resnes 00:08:29 Chatter shared under Creative Commons Attribution-NonCommercial 3.0 Unported license at http://vodo.net/Chatter
Clickin' For Love Pablo Pappano 01:28:39 Clickin' For Love shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vodo.net/clickinforlove
Climate Cycle Paul O'Connor 00:21:41 Climate Cycle shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vodo.net/ccycle
Cloudland LateNite Films 00:11:41 Cloudland shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/17105083
Cold Bahadir Karasu 00:21:23 Cold shared under Creative Commons Attribution-ShareAlike 3.0 Unported license at http://vimeo.com/41402223
Copyright is for losers Ninotchka Art Project 00:24:39 Copyright is for losers shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vimeo.com/2026149
Couchsurf Georg Boch 00:18:51 Couchsurf shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License license at http://vodo.net/couchsurf
Crooked Features Mike Peter Reed 01:25:01 Crooked Features shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vodo.net/crookf
Damaged Kung-Fu Juliane Block 00:16:54 Damaged Kung-Fu shared under Creative Commons Attribution Share Alike 3.0 license at http://www.filmannex.com/movie/damaged-kung-fu/30279
Dead Man Drinking Rohan Harris 01:28:26 Dead Man Drinking shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://www.deadmandrinking.com/
Decay CERN by physics PhD students 01:16:06 Decay shared under Creative Commons Attribution-ShareAlike 3.0 Unported license at http://vimeo.com/55157792
Deceived Winkler Pictures 01:31:01 Deceived shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/39057892
Dimensional Meltdown Ofer Pedut 00:07:34 Dimensional Meltdown shared under Creative Commons Attribution-NonCommercial 3.0 Unported license at http://vodo.net/dimensional
Down With the King Michael Wolcott 00:30:33 Down With the King shared under Creative Commons Attribution-NonCommercial 3.0 Unported license at http://vodo.net/dwtk
Elephant's Dream Bassam Kurdali 00:10:53 Elephant's Dream shared under Creative Commons Attribution 3.0 license at http://orange.blender.org/
Emperor Juliane Block & Adrian Lai 01:35:56 Emperor shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vodo.net/emperor
END:CIV Franklin López 01:16:45 END:CIV shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vodo.net/endciv
Fall and Love Jordan Baker 00:12:43 Fall and Love shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/52084858
First Bite Dead Flower Productions 00:10:40 First Bite shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/23980578
Four Eyed Monsters Arin Crumley & Susan Buice 01:11:57 Four Eyed Monsters shared under Creative Commons Attribution-ShareAlike 3.0 Unported license at http://vodo.net/foureyedmonsters
Full Service Ian Quill 00:18:41 Full Service shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/22719579
Good Boys go to Heaven and Bad Boys go to Europe Fabrice Renucci 01:08:37 Good Boys go to Heaven and Bad Boys go to Europe shared under Creative Commons Attribution 3.0License license at http://vodo.net/goodboys
Here. My Explosion... Jeffery Davis, Eleese Longino & Seth Burnham 01:14:50 Here. My Explosion... shared under Creative Commons Attribution-ShareAlike 3.0 Unported license at https://www.createspace.com/267787
Home Yann Arthus-Bertrand 01:33:17 Home shared under Creative Commons Attribution-ShareAlike 3.0 Unported license at http://archive.org/details/Home2009
How Fear Came Anaïs Caura & Bulle Tronel 00:10:06 How Fear Came shared under Creative Commons Attribution-NonCommercial 3.0 Unported license at http://vimeo.com/42690350
Interferencies Debt Observatory (ODG) and Quepo 01:13:49 Interferencies shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://www.interferencies.cc/
Iron Sky Teaser 3: We Come In Peace Stealth Media Group 00:01:00 Iron Sky Teaser 3 We Come In Peace shared under Creative Commons Attribution-ShareAlike 3.0 Unported license at http://www.ironsky.net/
Islands Diego Contreras 00:02:53 Islands shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/50512824
Je suis ce que je vois Simon Bonneau 00:02:21 Je suis ce que je vois shared under Creative Commons Attribution Non-Commercial Share Alike 3.0 license at http://www.youtube.com/user/TheChivteam
Jiminy JakkintheBox 00:08:49 Jiminy shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/21791346
L.U.C.K Daniel Cooper 00:10:31 L.U.C.K shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/37041387
Le Fear Jason Croot 01:01:32 Le Fear shared under Creative Commons Attribution 3.0 Unported license at http://vodo.net/lefear
Lesson Learned Fritz Joseph 00:12:58 Lesson Learned shared under Creative Commons Attribution-ShareAlike 3.0 Unported license at http://vimeo.com/40539260
The Lionshare Josh Bernhard 01:08:16 The Lionshare shared under Creative Commons Attribution-NonCommercial 3.0 Unported license at http://vodo.net/lionshare
Lo que tu Quieras Oir Guillermo Zapata 00:10:15 Lo que tu Quieras Oir shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://creativecommons.org/weblog/entry/7537
Lusty Little Heart of Mine Martin Heuser 00:18:30 Lusty Little Heart of Mine shared under Creative Commons Attribution-NonCommercial 3.0 Unported license at http://vodo.net/lusty
Monolog Eray Dinc 01:17:36 Monolog shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/20235811
Nasty Old People Hanna Sköld 01:24:25 Nasty Old People shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://nastyoldpeople.blogspot.fr/
Norm Elle Marsh, Anton Bernardino, Vanessa Caitlin Goh & Jackson Rouse 00:06:30 Norm shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/43513380
Nuclear Family Dominic Mercurio 00:28:20 Nuclear Family shared under Creative Commons Attribution-NonCommercial 3.0 Unported license at https://www.facebook.com/nuclearfamilymovie
Oceania Harry Dehal 00:54:19 Oceania shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 United StatesLicense license at http://www.hdehal.com/filmandvideo.php
Of Games and Escapes Bevan Klassen 01:17:05 Of Games and Escapes shared under Creative Commons Attribution-NonCommercial 3.0 Unported license at http://vodo.net/GamesEscapes
On Time Todd Wiseman 00:05:11 On Time shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://www.youtube.com/watch?v=8zI2JRLFQoE
Origami ESMA MOVIES 00:08:20 Origami shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/52560308
Parafundit Riccardo Melato 00:13:10 Parafundit shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vimeo.com/50060660
Payload Stu Willis 00:17:55 Payload shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/50509389
Pennipotens Heather Freeman 00:17:38 Pennipotens shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://pennipotens.blogspot.fr/
Pioneer One S01E01 Bracey Smith 00:34:04 Pioneer One S01E01 shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vodo.net/pioneerone
Pioneer One S01E02 Bracey Smith 00:38:41 Pioneer One S01E02 shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vodo.net/pioneerone
Pioneer One S01E03 Bracey Smith 00:31:46 Pioneer One S01E03 shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vodo.net/pioneerone
Pioneer One S01E04 Bracey Smith 00:34:16 Pioneer One S01E04 shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vodo.net/pioneerone
Pioneer One S01E05 Bracey Smith 00:40:15 Pioneer One S01E05 shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vodo.net/pioneerone
Pioneer One S01E06 Bracey Smith 00:44:34 Pioneer One S01E06 shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vodo.net/pioneerone
Point Of Departure Matthias Merkle 01:33:19 Point Of Departure shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://wiki.creativecommons.org/Point_Of_Departure
Riding the Rails Juan Soto 00:15:00 Riding the Rails shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/17465105
Riflessi Emanuela Ponzano 00:20:52 Riflessi shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vimeo.com/31900322
RIP! A Remix Manifesto Girl Talk, Lawrence Lessig, Gilberto Gil & Cory Doctorow 01:27:20 RIP! A Remix Manifesto shared under Creative Commons Attribution-NonCommercial 3.0 Unported license at http://creativecommons.org/tag/rip-a-remix-manifesto
Rising Tide Philip Shotton & Dawn Furness 01:18:52 Rising Tide shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License license at http://www.risingtidethemovie.com/
Roskilde The Experience Per Tore Holmberg 00:22:15 Roskilde The Experience shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://www.roskilde-experience.com/
Santa Cruz Beach Boardwalk Eugenia Loli 00:04:54 Santa Cruz Beach Boardwalk shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/2886954
Scumbabies Joseph R. Lewis 01:31:03 Scumbabies shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vodo.net/scumbabies
Seven Desmond Bowles & Dave Dornbrack 00:16:40 Seven shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vimeo.com/55103414
Sintel Martin Lodewijk 00:14:48 Sintel shared under Creative Commons Attribution 3.0 license at www.sintel.org
Sita Sings the Blues Nina Paley 01:21:31 Sita Sings the Blues shared under Creative Commons Attribution-ShareAlike 3.0 Unported license at http://www.sitasingstheblues.com/
Sky in Slow motion: Fetch! Jenifer Avila 00:03:59 Sky in Slow motion: Fetch! shared under Creative Commons Attribution-NonCommercial 3.0 Unported license at http://www.youtube.com/watch?v=aG6R771H1FQ
Solace Daniel Cooper 00:10:05 Solace shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/42505454
Spaceman Jono Schaferkotter & Before North 00:15:29 Spaceman shared under Creative Commons Attribution-NonCommercial 3.0 Unported License license at http://vodo.net/spaceman
Steal this film The League of Noble Peers 00:51:30 Steal this film shared under Creative Commons Attribution 3.0 Unported license at http://vodo.net/stf
Sugar Andrew John Rainnie 00:22:27 Sugar shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vodo.net/Sugar
Superhero Langley McArol 00:19:01 Superhero shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/23423341
Suspicious Minds Tizaster Productions 00:08:07 Suspicious Minds shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/8833583
Sweet Hills NotWorkingFilms 00:16:48 Sweet Hills shared under Creative Commons Attribution-NonCommercial 3.0 Unported license at http://vimeo.com/51445616
Tears of steel Ian Hubert & Ton Roosendaal 00:12:14 Tears of steel shared under Creative Commons Attribution 3.0 Unported license at http://www.tearsofsteel.org/
The Box BK 00:27:47 The Box shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/3610952
The Cosmonaut (Trailer) Nicolás Alcalá 00:02:21 The Cosmonaut (Trailer) shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) license at http://www.thecosmonaut.org/
The Dabbler Reid Gershbein 00:59:59 The Dabbler shared under Creative Commons Attribution-ShareAlike 3.0 Unported license at http://www.royalbaronialtheatre.com/blog/the-dabbler-film-details-2wkfilm.html
The frame of the dead Samuel Sebastian 01:48:37 The frame of the dead shared under Creative Commons Attribution 3.0 Unported license at http://vodo.net/framedead
The Graduates Ryan Gielen 01:31:08 The Graduates shared under Creative Commons Attribution-NonCommercial 3.0 Unported license at http://vodo.net/thegraduates
The Great Commandment Maurice Moscovitch 01:20:12 The Great Commandment shared under Public Domain License license at http://www.youtube.com/watch?v=keOrz7n7ETo
The idea Berthold Bartosch 00:25:21 The idea shared under Public Domain License license at http://vimeo.com/20866713
The Immortals Matthias Merkle & Antje Borchardt 01:34:59 The Immortals shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://wiki.creativecommons.org/The_Immortals
The Manifesto Mr Nobody 01:30:32 The Manifesto shared under Creative Commons Attribution-ShareAlike 3.0 Unported License license at http://vodo.net/Manifesto
The Mapmaker Twisty-Headed Man Company 00:26:17 The Mapmaker shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vodo.net/mapmaker
The Master Plan Aron Campisano 01:44:20 The Master Plan shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://themasterplanfilm.com/
The room of Franz Kafka Fred. L'Epee 00:04:09 The room of Franz Kafka shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vimeo.com/14482569
The secret number Colin Levy 00:15:31 The secret number shared under Creative Commons Attribution-NonCommercial 3.0 Unported license at http://vimeo.com/43732205
Time Expired Nick Lawrence & Rachel Tucker 00:32:42 Time Expired shared under Creative Commons Attribution-NonCommercial 3.0 Unported license at http://vodo.net/timeexpired
To Claire From Sonny Ennui Pictures 00:06:54 To Claire From Sonny shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://www.youtube.com/watch?v=8rKW-VRFczA
To Kill A King Run Productions 00:21:01 To Kill A King shared under Creative Commons Attribution-NonCommercial 3.0 Unported license at http://vimeo.com/30847762
Torno Subito Simone Damianiunder 01:29:07 Torno Subito shared under Creative Commons Attribution-NonCommercial 3.0 Unported license at http://creativecommons.org/tag/torno-subito
Valkaama Tim Baumann 01:33:13 Valkaama shared under Creative Commons Attribution-ShareAlike 3.0 Unported license at http://www.valkaama.com/
Viaje a la tierra del Quebracho TEMBE Cooperativa 00:11:53 Viaje a la tierra del Quebracho shared under Creative Commons Attribution-ShareAlike 3.0 Unported license at http://vodo.net/quebracho
Waldo the Dog Kris Canonizado 02:00:39 Waldo the Dog shared under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license at http://vodo.net/WaldotheDog
Wanted Ezel Domanic 00:01:57 Wanted shared under Creative Commons Attribution 3.0 Unported license at http://www.filmannex.com/movie/wanted/30133
When Rabbits Fly Helgi Johannsson 00:28:18 When Rabbits Fly shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/58619416
You Again Lauren Teng 00:14:30 You Again shared under Creative Commons Attribution 3.0 Unported license at http://vimeo.com/33454078

About

In contrast to existing datasets with very few video resources and limited accessibility due to copyright constraints, LIRIS-ACCEDE consists of videos with a large content diversity annotated along affective dimensions. All excerpts are shared under Creative Commons licenses and can thus be freely distributed without copyright issues. The dataset (the video clips, annotations, features and protocols) are publicly available.

Download

To acquire access to the database:

  • Please print the End User License Agreement (EULA, download here).
  • Sign it, and scan it.
  • Send a mail to accede@liris.cnrs.fr and include the signed EULA as a pdf.

 

Please note, that any requests from free email addresses (hotmail, yahoo, gmail, etc.) will be refused. After submission of the form, it may take up to a week for you to receive the download link (you will receive a notification by email).

 

If you have any comments or questions about LIRIS-ACCEDE that are not covered in the database section, do not hesitate to email accede@liris.cnrs.fr.

Thank you for your interest in LIRIS-ACCEDE.

Sources

The discrete and continuous collections of LIRIS-ACCEDE have been created by a french team of researchers:

 

Finally, we want to thank Léo Perrin who created the program generating the comparisons and collecting the data from CrowdFlower, Xingxian Li for his help on the modification of the GTrace program and we further would like to thank Ting Li who worked on the correlation between continuous affective ratings and physiological measurements. Of course, we also would like to thank all film-makers that shared their work under Creative Commons licenses.

The data for the MediaEval 2015 affective impact of movies task has been collected by:

 

Violence and affective classes could not have been collected without the effort from all the task organizers. Special thanks go to Bogdan Ionescu's team (University Politehnica of Bucharest, Romania), Hanli Wang's team (Tongji University, China), Vu Lam Quan's team (University of Science, VNU-HCMC, Vietnam), and Markus Schedl's team (Johannes Kepler University, Linz, Austria), who contributed a lot to the annotations, and of course Mats Sjöberg who organized... almost everything!

 

This work was supported in part by the French research agency ANR through the VideoSense Project under the Grant 2009 CORD 026 02 and through the Visen project within the ERA-NET CHIST-ERA framework under the grant ANR-12-CHRI-0002-04.