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On the use of visual analysis technology to search audiovisual collections for research in the digital humanities. The presentation explains the audiovisual archive approach wrt access in general using visual analysis and discusses how this could fit into the practice of DH research on the basis of the results of the FP7 project AXES.
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ACCESSING LARGEAUDIOVISUAL COLLECTIONS
USING VISUAL ANALYSISAV IN DH WORKSHOP @ DH2014 LAUSANNE
ROELAND ORDELMAN
NETHERLANDS INSTITUTE FOR SOUND AND VISION
BUSINESS ARCHIVE DUTCH PUBLIC BROADCASTERS
LARGE DIGITIZATION PROGRAMS
CLARIAH PRESENTATIE 11 September 2013
6
+800.000 hours of audiovisual content
‘POTENTIAL’
Find what you were (not) looking for
Browse video to find what you were looking for
X
We need labels!
Labels connect(content, context)
Labeling
CLARIAH PRESENTATIE 11 September 2013
13
BIG DATA!
CLARIAH PRESENTATIE 11 September 2013
14
INNOVATIVE PLATFORMS
We need USEFUL labels
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USEFUL?
Developer/ICT researcher
DH Researcher
17
FEEDBACK
Research & Education
Broadcast Professionals
Hergebruik
Media Archivists (documentalisten)
Beschrijven
Journalists Research
Academic researchers Investigate
Education Illustrate
19
Use Scenarios&
System Requirements
Interview & elicitation sessions
Mock-up creation & evaluation
Prototype evaluation
System evaluation
Surveys & log analysis
Qualitative
Qualitative
QualitativeQuantitative
Quantitative/Qualitative
BUILDING PROTOTYPES
2012
2013
-PRO
-RES
Onderzoekers
Media Professionals
<nisv@axes> ls –lTotal 10-r--r--r--. 1 nisv axes 301 Jun 26 2011 METADATA-r--r--r--. 1 nisv axes 301 Jun 26 2011 SUBTITLES-r--r--r--. 1 nisv axes 301 Jun 26 2011 SPEECH RECOGNITION-r--r--r--. 1 nisv axes 301 Jun 26 2011 FACE RECOGNITION-r--r--r--. 1 nisv axes 301 Jun 26 2011 VISUAL CONCEPT DETECT-r--r--r--. 1 nisv axes 301 Jun 26 2011 EVENT DETECTION-r--r--r--. 1 nisv axes 301 Jun 26 2011 LOCATION DETECTION-r--r--r--. 1 nisv axes 301 Jun 26 2011 QUERY BY EXAMPLE-r--r--r--. 1 nisv axes 301 Jun 26 2011 SEARCH-r--r--r--. 1 nisv axes 301 Jun 26 2011 RECOMMENDATION-r--r--r--. 1 nisv axes 301 Jun 26 2011 USER INTERFACE<nisv@axes> |
Face Recognition
Query by example
DETECTION REQUIRES TRAINING(EXAMPLES)
2nd EC review meeting – Hilversum – Mar 19th 2013
2nd EC review meeting – Hilversum – Mar 19th 2013
EXPECTATION MANAGEMENT
2nd EC review meeting – Hilversum – Mar 19th 2013
Expectation Management
• Expectation management:– Training examples versus result list– Google images search versus visual search in AV
• Understanding visual search:– why something is hard to detect
• visual characteristics, training examples
– Noise is not bad per definition
DH perspective
• First explorations in various projects– Requirements studies– Demonstrations– Prototypes
• Technology is ready to start exploring its use in real use scenarios (e.g., query by example)
• Feed DH ideas into ICT research community
Technology exists that could helpTechnology does not solve all
problemsDiscuss with ICT experts
Technology has a price, what is the RoI?
AWARENESS
How does technology fit inHow do limitations fit in
‘Technology Critique’ (Historian 2.0?)ICT and curriculum
METHODOLOGY/TRAINING
What can it do?How does it work?
How does it perform?How can it be improved?
MICRO MACRO
How can we use it?What do we need?How does it scale?
Who could benefit as well?
www.axes-project.euroelandordelman.nl
Questions?
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