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AXES project use case
17th November 2015
Multimedia document processing
using the WebLab platform
Yann Mombrun – Bruno Grilhères
Advanced Information Processing
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Advanced Information Processing
• Airbus Defence and Space
• Airbus Group subsidiary
• With Airbus and Airbus Helicopters
• Advanced Information Processing team (TCOIC4)
•10 engineers and Ph.D students
•R&T/R&D for unstructured document analysis (and data
fusion)
Multimedia document processing using the WebLab platform
17th November 2015 2
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Unstructured document analysis
From unstructured data...
… to structured and actionable knowledge
• Complex processing chain: data gathering, data normalisation, translation,
information extraction, information fusion, indexing and search…
• Data variability: media, format, language…
è Need an integration platform
Multimedia document processing using the WebLab platform
17th November 2015 3
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What is WebLab?
An integration platform
• allowing the integration of a selection of
software components (search engine,
information extraction, translation,
knowledge management, graphical
representation using maps/networks, etc.)
• allowing the interoperation of the selected
components.
• based on recognised standards (SOA, Web
Services, Semantic Web)
• enabling the composition of services inside
complex processing chains
A set of media mining services for documentprocessing to be
reused in all the WebLab projects.
Multimedia document processing using the WebLab platform
17th November 2015 4
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What is WebLab?
Internal studies
Multimedia document processing using the WebLab platform
17th November 2015 5
Customer oriented projects
A range of projects in the fields of media-mining, NLP & KM
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Content providers and end-users
Technical
partners
Administrative coordination
Focus on the AXES Project
17th November 2015
Multimedia document processing using the WebLab platform
6
FP7 ICT 2011-2014 http://www.axes-project.eu
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Not just search:
ExploreBrowse Experience
A ( u s e r / c e n t e r e d ( a p p r o a c h
Our$aim$is$to$open$up$audiovisual$digital$libraries,$increasing$their$cul=
an$early$stage.$Targeted$users$are$media$professionals,$educators,$stu=
dents,$amateur$researchers$and$home$users.
Developing tools providing
new engaging ways to interact
with audiovisual libraries…
17th November 2015
Multimedia document processing using the WebLab platform
7
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... for a multitude of end users...
Media professionals
Researchers
Home users
!
!
!
Intelligence Officers
17th November 2015 8
Multimedia document processing using the WebLab platform
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… building on state-of-the-art
content analysis techniques…
Computer vision
Speech recognition /
audio analysis
Search & navigation
Weakly-supervised methods
d to define
on. Simi-
constraints
fier. These
diction and
approaches
n addition,
d to define
eline to ex-
1, see Fig-
to present
om the ex-
ack identi-
first use a
en link the
proach for
ncontrolled
Figure 1. An overview of our processing pipeline. (a) A face de-
tector is applied to each video frame. (b) Face tracks are created
by associating face detections. (c) Facial points are localized. (d)
Locally SIFT appearance descriptors are extracted on the facial
features, and concatenated to form the final face descriptor.
nose, see Figure 1. We then extract SIFT descriptors at
these nine locations at three different scales, which we con-
catenate to form a feature vector f ∈ IRD
of dimension
D = 3 × 9 × 128 = 3456. As the descriptors are computed
at facial feature points, it is robust to pose and expression
changes. Using the SIFT descriptor makes it also robust to
small errors in localization.
3.2. Metric learning from face tracks
Given a set of face tracks we can extract face pairs from
them to learn a metric over the face descriptors in an unsu-
verview of our processing pipeline. (a) A face de-
d to each video frame. (b) Face tracks are created
face detections. (c) Facial points are localized. (d)
appearance descriptors are extracted on the facial
ncatenated to form the final face descriptor.
ure 1. We then extract SIFT descriptors at
ations at three different scales, which we con-
rm a feature vector f ∈ IRD
of dimension
128 = 3456. As the descriptors are computed
re points, it is robust to pose and expression
ng the SIFT descriptor makes it also robust to
17th November 2015 9
Multimedia document processing using the WebLab platform
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… building on state-of-the-art
content analysis techniques…
People
Places
Object categories
Events
17th November 2015 10
Multimedia document processing using the WebLab platform
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AXES final architecture
Multimedia document processing using the WebLab platform
Normalisation
Shot
Detection
Text
Processing
Speech
Processing
Video
Content
Linking
Messaging and Distribution
Orchestration
Image
Indexing
Text
Indexing
Text, Links
and Image
Indices
AXES Pro UI
AXES Home UI
AXES Research UI
17th November 2015 11
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OpenAXES OW2 project
• Free (or) Open source version of AXES è OW2 WebLab subproject
• Proposed to EC and asked by end users
• Demonstration purposes (by Airbus and AXES partners)
• Baseline for a following project
• Using partners code or replace by similar components
• Not fully Open source yet (TEC and KUL)
• Complementary with WebLab demonstration
è Focus on multimedia processing
Multimedia document processing using the WebLab platform
17th November 2015 12
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OpenAXES
Free (or) Open Source Version of AXES
Based on OW2 WebLab
Already available in the current version :
• Video Normalisation (Airbus DS / FFMpeg)
• Shot/Scene detection (TEC)
• Image concept extraction (KUL)
• Spoken word & Metadata search (UT)
• Speech to Text (En&Fr) (Airbus DS / Sphinx / LIUM)
• Similar search (Airbus DS / Pastec)
• On-the-fly search (UO)
• Favorites / Like/ Most viewed / Video cutter (DCU)
• Recommendations (UT)
• Easy installation on Ubuntu 14.04, 15.10 and Mint 17
17th November 2015
Multimedia document processing using the WebLab platform
13
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Demonstration
Multimedia document processing using the WebLab platform
17th November 2015 14
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Questions?
Multimedia document processing using the WebLab platform
17th November 2015 15
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The WebLab Platform
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OpenAXES Links
• Hosted on OW2 Forge http://forge.ow2.org/projects/openaxes/
• Deliverables Versions
http://forge.ow2.org/project/showfiles.php?group_id=436
– OpenAXES-1.1.0 2015-07-07
– OpenAXES-1.0.1 2015-05-19
– OpenAXES-1.0.0 2015-04-01
– OpenAXES-0.6.0 2015-02-16
– OpenAXES-0.5.0 2015-01-30
– OpenAXES-0.2.0 2014-12-16
• SVN svn://svn.forge.objectweb.org/svnroot/openaxes
• Continuous Build http://bamboo.ow2.org/browse/OPENAXES
• Bug Tracking http://jira.ow2.org/browse/OPENAXES
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Visor: On-the-fly object detection and retrieval
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AXES Datasets
• Year 1
• Mix of NISV, DW and BBC content: 40h
• Year 2
• NISV PRO: 400h
• BBC Eastenders:500h
• BBC PRO: 480h
• Year 3
• BBC Home: 1000h
• DW RES: 100h
• NISV RES: 1000h
• Year 4
• NISV Home: 1000h
• DW Home: 200h
• BBC Benchmarks:400h
• CNN Internet Archive: 100h

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