SlideShare a Scribd company logo
The 2012 Social Event Detection Dataset
Symeon Papadopoulos1, Emmanouil Schinas1, Vasileios Mezaris1,
Raphaël Troncy2, Yiannis Kompatsiaris1

1
  CERTH-ITI, Thessaloniki, Greece
2
  EURECOM, Sophia Antipolis, France


Oslo, 28 Feb - 1 Mar 2013
SED2012 Overview
• Large collection (>160K) of CC-licensed Flickr
  photos and some of their metadata
• Event annotations for 149 target events (of
  specific categories and locations of interest)

• Primary use: Social event detection
  – Used in the context of MediaEval 2012 (SED task)
• Secondary uses: image geotagging,
  distractors in CBIR, city summarization
                                      2
Dataset Overview
Flickr photo collection
• 167,332 photos
• 4,422 unique contributors
• Creative Commons licenses

Event Annotations
• Challenge 1: Technical events in Germany
• Challenge 2: Soccer events in Hamburg and Madrid
• Challenge 3: Indignados movement events in Madrid

                                      3
Data Collection Process
• Flickr API: http://www.flickr.com/services/api/
• Used method flickr.photo.search with five
  geographical centres:
   Barcelona, Cologne, Hamburg, Hannover, Madrid
• Time period: Jan 2009 – Dec 2011
• All photos CC licensed
• 403 photos from the
       EventMedia collection
      R. Troncy, B. Malocha, and A. Fialho. Linking Events with Media. In 6th Intern.
      Conference on Semantic Systems (I-SEMANTICS), Graz, Austria, 2010

                                                                    4
Photo Distribution
Place distribution



Yearly distribution



Language distribution



                        5
Dataset Collection Motivation
Selection of five cities (three German, two Spanish):
• Include large number of non-English text metadata (cf.
   language distribution table)
• Ensure existence of numerous events for the target types
• Include distractor images:
   – Challenge 2: Cologne, Hannover distractor for Hamburg, Barcelona
     distractor for Madrid
   – Challenge 3: Barcelona distractor for Madrid
Selection of only geotagged photos:
• Ease of annotation
Selection of only CC-licensed photos:
• Reuse of collection for research

                                                      6
Tag Statistics           (1/2)
                           number of users using the tag

51,611 unique tags

prevalence of
location specific tags




event-specific tags


                                            7
Tag Statistics                    (2/2)
                                       barcelona
>20K photos have no tags                    spain
                                                    madrid



                                                             >57% of tags appear
                                                                   once or twice




 83.9% less than or equal to 10 tags      >40K tags appear less than 10 times


                                                         8
User Statistics




                                       60% of users less
                                       than 10 photos




           30 most active users contribute ~30% of dataset
                                            9
Ground Truth Creation
• Manual annotations by use of CrEve
  – web-based annotation
  – two-round annotation by five annotators (three in the
    first, two in the second)
  – interactive annotation (search & annotate)
  – each round terminated as soon as no new event-related
    photos discovered
  – approximate effort: 100 person-hours
   C. Zigkolis, S. Papadopoulos, G. Filippou, Y. Kompatsiaris, A. Vakali. Collaborative Event
   Annotation in Tagged Photo Collections. Multimedia Tools & Applications, 2012


• Annotations for Challenge 1 enriched by EventMedia
  (403 photos featuring technical events in Germany)
                                                                        10
Ground Truth Statistics (1/3)




           10 events related
           with >100 photos

                               ~27% of events associated
                                      with 1 or 2 photos


                                     11
Ground Truth Statistics (2/3)
106 events are captured by
single users
                                 erroneous timestamps in photos




     9 events captured by more   The majority of events last for less
     than 10 people              than a day (typical for soccer)
                                               12
Ground Truth Statistics (3/3)
 Madrid events

                      Santiago Bernabeu
                      stadium              Puerta del Sol




Stadium of Butarque



                      Vicente Calderon stadium
                                                 13
Technical Event Examples
PHP Unconf. 2010           Gamescom 2009




              CeBIT 2010                   Convention Camp 2011




                                                      14
Soccer Event Examples
Real Madrid – Milan (2010)          World Cup 2010




                    St. Pauli – HSV (2010)           Spain – Colombia (2011)




                                                              15
Indignados Event Examples
Inaugural march, 15 May         Large gathering, 20 May




            Gathering, 15 Oct               Demonstration, 17 Nov




                                                          16
Evaluation
• F-measure (macro), Precision, Recall
  – goodness of retrieved photos, but not how well
    they were clustered into events
• Normalized Mutual Information (NMI)
  – compares automatically extracted clustering of
    photos into events with the ground truth
• Evaluation script is made available together
  with the dataset.
• Implementation of event detection available:
          http://mklab.iti.gr/project/sed2012_certh
                                       17
Questions
 @sympapadopoulos
 www.slideshare.net/sympapadopoulos

More Related Content

PDF
Fine tuning a convolutional network for cultural event recognition
PPTX
Image Retrieval at the BnF
PDF
Warning - Real Time Global Air Quality Display: case study of digital art and...
PDF
ECIR 2013 Keynote - Time for Events
 
PDF
巨量與開放資料之創新機會與關鍵挑戰-曾新穆
PDF
The role of geospatial information in a hyper connected society
PDF
The role of geospatial information in a hyper connected society
PDF
The role of geospatial information in a hyper connected society
Fine tuning a convolutional network for cultural event recognition
Image Retrieval at the BnF
Warning - Real Time Global Air Quality Display: case study of digital art and...
ECIR 2013 Keynote - Time for Events
 
巨量與開放資料之創新機會與關鍵挑戰-曾新穆
The role of geospatial information in a hyper connected society
The role of geospatial information in a hyper connected society
The role of geospatial information in a hyper connected society

Similar to SED2012 Dataset (20)

PPT
VRCAI 2011 Billinghurst Keynote
PDF
(Linked Data Development and Exploitation track) "Generating the Semantic Sna...
PDF
Computer Vision++: Where Do We Go from Here?
PDF
3D Printing: GIS Day 2013 Work in Progress Report
PPTX
Multimedia rescue 161018
PPTX
From Research to Applications: What Can We Extract with Social Media Sensing?
PPTX
Jan Hendrik Hammer, Fraunhofer, KIT, Eyetracking and Gaze Analysis
PPTX
News Semantic Snapshot
PDF
Klipfolio - Your Swiss Knife on data
PDF
Smart Data fo the Smart Cities and Smart Factories in the future
PDF
Analyzing large multimedia collections in an urban context - Prof. Marcel Wor...
PDF
Using synthetic data for computer vision model training
PPTX
Information Fusion Methods for Location Data Analysis
PPTX
Web and Social Media Image Forensics for News Professionals
PPTX
Media REVEALr: A social multimedia monitoring and intelligence system for Web...
PDF
Visual Information Analysis for Crisis and Natural Disasters Management and R...
PDF
A Large-Scale Analysis of YouTube Videos Depicting Everyday Thermal Camera Use
PPTX
Mediarevealr: A social multimedia monitoring and intelligence system for Web ...
PPTX
Listening to the pulse of our cities fusing Social Media Streams and Call Dat...
PDF
COSC 426 Lecture 1: Introduction to Augmented Reality
VRCAI 2011 Billinghurst Keynote
(Linked Data Development and Exploitation track) "Generating the Semantic Sna...
Computer Vision++: Where Do We Go from Here?
3D Printing: GIS Day 2013 Work in Progress Report
Multimedia rescue 161018
From Research to Applications: What Can We Extract with Social Media Sensing?
Jan Hendrik Hammer, Fraunhofer, KIT, Eyetracking and Gaze Analysis
News Semantic Snapshot
Klipfolio - Your Swiss Knife on data
Smart Data fo the Smart Cities and Smart Factories in the future
Analyzing large multimedia collections in an urban context - Prof. Marcel Wor...
Using synthetic data for computer vision model training
Information Fusion Methods for Location Data Analysis
Web and Social Media Image Forensics for News Professionals
Media REVEALr: A social multimedia monitoring and intelligence system for Web...
Visual Information Analysis for Crisis and Natural Disasters Management and R...
A Large-Scale Analysis of YouTube Videos Depicting Everyday Thermal Camera Use
Mediarevealr: A social multimedia monitoring and intelligence system for Web ...
Listening to the pulse of our cities fusing Social Media Streams and Call Dat...
COSC 426 Lecture 1: Introduction to Augmented Reality
Ad

More from Symeon Papadopoulos (20)

PDF
DeepFake Detection: Challenges, Progress and Hands-on Demonstration of Techno...
PDF
Deepfakes: An Emerging Internet Threat and their Detection
PDF
Knowledge-based Fusion for Image Tampering Localization
PDF
Deepfake Detection: The Importance of Training Data Preprocessing and Practic...
PPTX
COVID-19 Infodemic vs Contact Tracing
PDF
Similarity-based retrieval of multimedia content
PPTX
Twitter-based Sensing of City-level Air Quality
PPTX
Aggregating and Analyzing the Context of Social Media Content
PDF
Verifying Multimedia Content on the Internet
PPTX
A Web-based Service for Image Tampering Detection
PPTX
Learning to detect Misleading Content on Twitter
PPTX
Near-Duplicate Video Retrieval by Aggregating Intermediate CNN Layers
PPTX
Verifying Multimedia Use at MediaEval 2016
PPTX
Multimedia Privacy
PPTX
Placing Images with Refined Language Models and Similarity Search with PCA-re...
PPTX
In-depth Exploration of Geotagging Performance
PPTX
Perceived versus Actual Predictability of Personal Information in Social Netw...
PPTX
Predicting News Popularity by Mining Online Discussions
PPTX
Finding Diverse Social Images at MediaEval 2015
PPTX
CERTH/CEA LIST at MediaEval Placing Task 2015
DeepFake Detection: Challenges, Progress and Hands-on Demonstration of Techno...
Deepfakes: An Emerging Internet Threat and their Detection
Knowledge-based Fusion for Image Tampering Localization
Deepfake Detection: The Importance of Training Data Preprocessing and Practic...
COVID-19 Infodemic vs Contact Tracing
Similarity-based retrieval of multimedia content
Twitter-based Sensing of City-level Air Quality
Aggregating and Analyzing the Context of Social Media Content
Verifying Multimedia Content on the Internet
A Web-based Service for Image Tampering Detection
Learning to detect Misleading Content on Twitter
Near-Duplicate Video Retrieval by Aggregating Intermediate CNN Layers
Verifying Multimedia Use at MediaEval 2016
Multimedia Privacy
Placing Images with Refined Language Models and Similarity Search with PCA-re...
In-depth Exploration of Geotagging Performance
Perceived versus Actual Predictability of Personal Information in Social Netw...
Predicting News Popularity by Mining Online Discussions
Finding Diverse Social Images at MediaEval 2015
CERTH/CEA LIST at MediaEval Placing Task 2015
Ad

Recently uploaded (20)

PPTX
Programs and apps: productivity, graphics, security and other tools
PPTX
Digital-Transformation-Roadmap-for-Companies.pptx
PDF
Unlocking AI with Model Context Protocol (MCP)
PDF
A comparative study of natural language inference in Swahili using monolingua...
PDF
Encapsulation theory and applications.pdf
PDF
Getting Started with Data Integration: FME Form 101
PDF
7 ChatGPT Prompts to Help You Define Your Ideal Customer Profile.pdf
PDF
A comparative analysis of optical character recognition models for extracting...
PDF
Encapsulation_ Review paper, used for researhc scholars
PDF
From MVP to Full-Scale Product A Startup’s Software Journey.pdf
PDF
Mushroom cultivation and it's methods.pdf
PDF
Univ-Connecticut-ChatGPT-Presentaion.pdf
PPTX
1. Introduction to Computer Programming.pptx
PDF
Zenith AI: Advanced Artificial Intelligence
PDF
gpt5_lecture_notes_comprehensive_20250812015547.pdf
PDF
DP Operators-handbook-extract for the Mautical Institute
PDF
Microsoft Solutions Partner Drive Digital Transformation with D365.pdf
PPTX
OMC Textile Division Presentation 2021.pptx
PDF
Hybrid model detection and classification of lung cancer
PPTX
Chapter 5: Probability Theory and Statistics
Programs and apps: productivity, graphics, security and other tools
Digital-Transformation-Roadmap-for-Companies.pptx
Unlocking AI with Model Context Protocol (MCP)
A comparative study of natural language inference in Swahili using monolingua...
Encapsulation theory and applications.pdf
Getting Started with Data Integration: FME Form 101
7 ChatGPT Prompts to Help You Define Your Ideal Customer Profile.pdf
A comparative analysis of optical character recognition models for extracting...
Encapsulation_ Review paper, used for researhc scholars
From MVP to Full-Scale Product A Startup’s Software Journey.pdf
Mushroom cultivation and it's methods.pdf
Univ-Connecticut-ChatGPT-Presentaion.pdf
1. Introduction to Computer Programming.pptx
Zenith AI: Advanced Artificial Intelligence
gpt5_lecture_notes_comprehensive_20250812015547.pdf
DP Operators-handbook-extract for the Mautical Institute
Microsoft Solutions Partner Drive Digital Transformation with D365.pdf
OMC Textile Division Presentation 2021.pptx
Hybrid model detection and classification of lung cancer
Chapter 5: Probability Theory and Statistics

SED2012 Dataset

  • 1. The 2012 Social Event Detection Dataset Symeon Papadopoulos1, Emmanouil Schinas1, Vasileios Mezaris1, Raphaël Troncy2, Yiannis Kompatsiaris1 1 CERTH-ITI, Thessaloniki, Greece 2 EURECOM, Sophia Antipolis, France Oslo, 28 Feb - 1 Mar 2013
  • 2. SED2012 Overview • Large collection (>160K) of CC-licensed Flickr photos and some of their metadata • Event annotations for 149 target events (of specific categories and locations of interest) • Primary use: Social event detection – Used in the context of MediaEval 2012 (SED task) • Secondary uses: image geotagging, distractors in CBIR, city summarization 2
  • 3. Dataset Overview Flickr photo collection • 167,332 photos • 4,422 unique contributors • Creative Commons licenses Event Annotations • Challenge 1: Technical events in Germany • Challenge 2: Soccer events in Hamburg and Madrid • Challenge 3: Indignados movement events in Madrid 3
  • 4. Data Collection Process • Flickr API: http://www.flickr.com/services/api/ • Used method flickr.photo.search with five geographical centres: Barcelona, Cologne, Hamburg, Hannover, Madrid • Time period: Jan 2009 – Dec 2011 • All photos CC licensed • 403 photos from the EventMedia collection R. Troncy, B. Malocha, and A. Fialho. Linking Events with Media. In 6th Intern. Conference on Semantic Systems (I-SEMANTICS), Graz, Austria, 2010 4
  • 5. Photo Distribution Place distribution Yearly distribution Language distribution 5
  • 6. Dataset Collection Motivation Selection of five cities (three German, two Spanish): • Include large number of non-English text metadata (cf. language distribution table) • Ensure existence of numerous events for the target types • Include distractor images: – Challenge 2: Cologne, Hannover distractor for Hamburg, Barcelona distractor for Madrid – Challenge 3: Barcelona distractor for Madrid Selection of only geotagged photos: • Ease of annotation Selection of only CC-licensed photos: • Reuse of collection for research 6
  • 7. Tag Statistics (1/2) number of users using the tag 51,611 unique tags prevalence of location specific tags event-specific tags 7
  • 8. Tag Statistics (2/2) barcelona >20K photos have no tags spain madrid >57% of tags appear once or twice 83.9% less than or equal to 10 tags >40K tags appear less than 10 times 8
  • 9. User Statistics 60% of users less than 10 photos 30 most active users contribute ~30% of dataset 9
  • 10. Ground Truth Creation • Manual annotations by use of CrEve – web-based annotation – two-round annotation by five annotators (three in the first, two in the second) – interactive annotation (search & annotate) – each round terminated as soon as no new event-related photos discovered – approximate effort: 100 person-hours C. Zigkolis, S. Papadopoulos, G. Filippou, Y. Kompatsiaris, A. Vakali. Collaborative Event Annotation in Tagged Photo Collections. Multimedia Tools & Applications, 2012 • Annotations for Challenge 1 enriched by EventMedia (403 photos featuring technical events in Germany) 10
  • 11. Ground Truth Statistics (1/3) 10 events related with >100 photos ~27% of events associated with 1 or 2 photos 11
  • 12. Ground Truth Statistics (2/3) 106 events are captured by single users erroneous timestamps in photos 9 events captured by more The majority of events last for less than 10 people than a day (typical for soccer) 12
  • 13. Ground Truth Statistics (3/3) Madrid events Santiago Bernabeu stadium Puerta del Sol Stadium of Butarque Vicente Calderon stadium 13
  • 14. Technical Event Examples PHP Unconf. 2010 Gamescom 2009 CeBIT 2010 Convention Camp 2011 14
  • 15. Soccer Event Examples Real Madrid – Milan (2010) World Cup 2010 St. Pauli – HSV (2010) Spain – Colombia (2011) 15
  • 16. Indignados Event Examples Inaugural march, 15 May Large gathering, 20 May Gathering, 15 Oct Demonstration, 17 Nov 16
  • 17. Evaluation • F-measure (macro), Precision, Recall – goodness of retrieved photos, but not how well they were clustered into events • Normalized Mutual Information (NMI) – compares automatically extracted clustering of photos into events with the ground truth • Evaluation script is made available together with the dataset. • Implementation of event detection available: http://mklab.iti.gr/project/sed2012_certh 17

Editor's Notes

  • #12: Events with 1 or 2 photos are much harder to detect, e.g. by methods based on clustering.