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Cécile
Picard‐Limpens

         Dr.
Computer
Science

        ccl.picard@gmail.com



     Freddy
Limpens

       Ph.D
Computer
Science

       freddy.limpens@inria.fr

How
will
I
get
rid
of


all
these
rusty
coffee
makers??

1.  Making
visible
on
the
web
the
stock
of
2nd
hand
shops

    ("ressourceries",
Emmaus
communiMes,
SalvaMon
Army,
etc.)

2.  Helping
these
organizaMons
digiMzing
and
cataloguing
their

    stock

3.  Enhancing
categorizaMons
and
search
in
catalogs
with
semanMc

    technologies


Training
data

                                                                                
clusters
of
objects


                                                           Hot
liquid
container
         associated


                                                                                          to

a
class

                                                                                                (tag)




                          2.
AUTOMATIC

                          shape
recogni;on
:

1.
Take
                  ‐ 
Find
closest
cluster



a
picture
               ‐ 
link
tag
to
object

                                                                          Seman;c
technologies

                                                                                
Set
of
ontologies

                                                                    describing
classes

of
objects


                                                                        (tags)
and
their
relaMons

                              3.
SEMI‐AUTOMATIC
                          Hot
liquid

                              refining
of
the
tagging
                     container

                              ‐ 
AutomaMcally
suggest


id
:


hl‐123456

tags
:
                       

related
tags
(ontology)
                                coffee
pot


hot
liquid
container

                              ‐ 
Manually
validate
or

       coffee
maker

☐
coffee
maker
                

correct
suggesMons

                                                                               tea
pot
     =
subClassOf

 
coffee
pot

☐
tea
pot


Hot
liquid
container




                      AUTOMATIC

                      shape
recogni;on
:

     Picture
         ‐ 
Find
closest
cluster


                      ‐ 
link
tag
to
object



                                                                           RELATED
WORK

                                            Image
analysis
tools
with
machine
learning

                                                   and
staMsMcal
modeling
techniques

•      FIRE
(Flexible
Image
Retrieval
Engine),
a
content‐based
image
retrieval
system 



Thomas
Deselaers,
RWTH
Aachen
University


•      LEAR
team:
visual
object
recogniMon
for
object
category
detecMon        





INRIA‐LJK
Grenoble



taking
into
account
shape,
color
or
texture
(via
opencv
library)
Université
de
Mons
&
numediart,
Belgium

                                                                     

•  Mediacycle:
allows
to
browse
image
libraries
by
organizing
them
into
clusters

Reinventing the Inventory
The
goal
:

 ?
        Finding
semanMcally


                   Related
tags


     ?

          To
enhance
searching

?





                   coffee
maker

The
idea
:

1.
/
Mapping
tags


With
ontologies’
concepts




                                      Hot
liquid

                                      container


                                                    coffee
pot

                             coffee
maker

                                            tea
pot

                                                         =
subClassOf

www.slideshare.net/fabien_gandon/web‐smanMque‐et‐web‐social‐1700977

www.slideshare.net/fabien_gandon/web‐smanMque‐et‐web‐social‐1700977

www.slideshare.net/fabien_gandon/web‐smanMque‐et‐web‐social‐1700977

The
idea
:

2.
/
Mining
semanMc
relaMons

From
tags’
structure
and
features





                           Coocurring


                                 tags



                String‐based
mapping

1. 
The
user
enter
"coffee
maker"





                                                          Results
for
"coffee
maker":


                 coffee
maker





   

    
   
    
2.   
The
system
suggests
addiMonal
results
thanks
to
semanMc
relaMons


                                                           Related
results
:


                                                            
Results
for
"tea
pot":





                                                            
Results
for
"coffee
pot":

•    An
automaMc
archiving
of
second‐hand
objects























     and
their
easy
retrieving
by
a
potenMal
user


•    A
good
picture
of
sustainable
development


•    All
the
techniques
used
aimed
to
be
free
and
open
source

•    Benchmark
current
shape
recogniMon
methods 
       
   
     
   


     on
our
specific
problem

•    Looking
for
available
ontologies/folksonomies
of
everyday

     objects
to
bootstrap
semanMc
funcMonnaliMes


•    PracMcal
experiment
in
a
«
ressourcerie
»
 
   
   
   
     
   


     (hnp://courtcircuioelleMn.wordpress.com/)


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Reinventing the Inventory

  • 1. Cécile
Picard‐Limpens
 Dr.
Computer
Science
 ccl.picard@gmail.com
 Freddy
Limpens
 Ph.D
Computer
Science
 freddy.limpens@inria.fr

  • 3. 1.  Making
visible
on
the
web
the
stock
of
2nd
hand
shops
 ("ressourceries",
Emmaus
communiMes,
SalvaMon
Army,
etc.)
 2.  Helping
these
organizaMons
digiMzing
and
cataloguing
their
 stock
 3.  Enhancing
categorizaMons
and
search
in
catalogs
with
semanMc
 technologies


  • 4. Training
data
 
clusters
of
objects

 Hot
liquid
container
 associated

 to

a
class
 (tag)
 2.
AUTOMATIC
 shape
recogni;on
:
 1.
Take
 ‐ 
Find
closest
cluster

 
a
picture
 ‐ 
link
tag
to
object
 Seman;c
technologies
 
Set
of
ontologies
 describing
classes

of
objects

 (tags)
and
their
relaMons
 3.
SEMI‐AUTOMATIC
 Hot
liquid
 refining
of
the
tagging
 container
 ‐ 
AutomaMcally
suggest

 id
:


hl‐123456
 tags
:
 

related
tags
(ontology)
 coffee
pot
 
hot
liquid
container
 ‐ 
Manually
validate
or

 coffee
maker
 ☐
coffee
maker
 

correct
suggesMons
 tea
pot
 =
subClassOf
  
coffee
pot
 ☐
tea
pot


  • 5. Hot
liquid
container
 AUTOMATIC
 shape
recogni;on
:
 Picture
 ‐ 
Find
closest
cluster

 ‐ 
link
tag
to
object
 RELATED
WORK
 Image
analysis
tools
with
machine
learning
 and
staMsMcal
modeling
techniques
 •  FIRE
(Flexible
Image
Retrieval
Engine),
a
content‐based
image
retrieval
system 
 Thomas
Deselaers,
RWTH
Aachen
University
 •  LEAR
team:
visual
object
recogniMon
for
object
category
detecMon 
 INRIA‐LJK
Grenoble
 taking
into
account
shape,
color
or
texture
(via
opencv
library) Université
de
Mons
&
numediart,
Belgium
 
 •  Mediacycle:
allows
to
browse
image
libraries
by
organizing
them
into
clusters

  • 7. The
goal
:
 ?
 Finding
semanMcally

 Related
tags

 ?
 To
enhance
searching
 ?
 coffee
maker

  • 8. The
idea
:
 1.
/
Mapping
tags

 With
ontologies’
concepts
 Hot
liquid
 container
 coffee
pot
 coffee
maker
 tea
pot
 =
subClassOf

  • 13. 1. 
The
user
enter
"coffee
maker"

 Results
for
"coffee
maker":
 coffee
maker
 

 
 
 
2. 
The
system
suggests
addiMonal
results
thanks
to
semanMc
relaMons
 Related
results
:
 
Results
for
"tea
pot":
 
Results
for
"coffee
pot":

  • 14. •  An
automaMc
archiving
of
second‐hand
objects






















 and
their
easy
retrieving
by
a
potenMal
user
 •  A
good
picture
of
sustainable
development
 •  All
the
techniques
used
aimed
to
be
free
and
open
source

  • 15. •  Benchmark
current
shape
recogniMon
methods 
 
 
 
 

 on
our
specific
problem
 •  Looking
for
available
ontologies/folksonomies
of
everyday
 objects
to
bootstrap
semanMc
funcMonnaliMes
 •  PracMcal
experiment
in
a
«
ressourcerie
»
 
 
 
 
 
 

 (hnp://courtcircuioelleMn.wordpress.com/)