nlpainter “text analysis for picture/movie generation”
DESCRIPTION
NLPainter “Text Analysis for picture/movie generation”. David Leoni Eduardo C á rdenas 12/01/2012. Motivation for choosing the project:. The purpose of our project is to transform text in images trying that both express the same mining. - PowerPoint PPT PresentationTRANSCRIPT
NLPainter
“Text Analysis for picture/movie generation”
David LeoniEduardo Cárdenas
12/01/2012
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MOTIVATION FOR CHOOSING THE PROJECT:
The purpose of our project is to transform text in images trying that both express the same mining.
• Adding illustrations to text can be of great help to memorize its contents• But searching images that represent the text is a time consuming task• Drawing entirely new images from scratch takes even longer.
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HOW THE PROBLEM CAN BE SOLVE?
In order to solve this problem we are going to use different techniques like text mining, natural language processing and semantic web:
We obtained a big Image database.
We have image with tags with the things that are inside of them.
We selected the most representative picture in our database that describes a specific object.
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HOW THE PROBLEM CAN BE SOLVE?
We used some text mining techniques in order to obtain entities, attributes, etc.
We used the PoS of the phrase that we want to convert to image.
We associated the text with the images.
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DATABASES
The following databases of images was used for our project:
LabelMe images are annotated with the shapes of the objects contained in the scene. labeling was done by unpaid users More than 70,000 shapes where obtained!
Animal Diversity Web •we fetched nearly 10000 pages.•1545 were information about animals.•3500 picture pages of animals (and for each picture page we extracted ~5 pics links) and 5000 were simply the pages about the hierarchy, needed to arrive to the information at the leaves•we fetched mammals,reptiles ,birds, bony fishes, insects, echinoderms, arthropods
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LabelMe
DATABASES
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Animal Diversity
DATABASES
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General Diagram:
Domain(Ani
mals)
Obtain andproce
ssText
Obtain and proces
spictur
es
Create
Ontology
and data
Query according
to the text
Image representing the text
Semanti
c Web
Image
processin
g
Obtain the text
Parse the text (NLP)
EntitiesAttributes
PathActions
Text ready for be
converted to image
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Specific Diagram (Text):
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Pictures in
Internet
Pictures in Databases (LabelMe Matlab)
XML to RDF
Retrieve Image
Image processi
ng
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Specific Diagram (Images):
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Specific Diagram (Ontology):
Domain(Animals)
Create the ontology
Extract linked data into asemantic repository
Semantic Web
Application
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Technologies and algorithms(Text)
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Programming Environment:Netbeans
Packages:Stanford Parser
Additional Packages: Image Generator
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Technologies and algorithms(Image)
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Programmation Language:MATLAB, Java
Programming Environment:Netbeans
Packages:LabelMeXOM
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Technologies and algorithms(Ontology)
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Editor:Protégé 4.1
RDF engine: OWLim Lite
Upper ontology: Wordnet
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Technologies and algorithms(General project)
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Programming Environment:NetBeans
RDF engine:OWLIM lite
Packages:XOM
Web server:Apache Tomcat 7.0 JSP
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Technologies and algorithms(General project)
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Documentation: Google Wiki
Versioning:SVN
Project Web Page:http://code.google.com/p/nlpainter/
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How to run the project?
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The Story Picturing Engine
A Text-to-Picture Synthesis System for Augmenting Communication
WordsEye
Comparison with other results:
Our Project working!19
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Some Results:
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Some Results:
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Some Results:
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Some Results:•The car and the sky, and the street.•The bike is at left of the car.•A person walking.•a person in the hotel.•the tree and a person.•a person in the water.•a door is to the right of a bed.•a door is to the left of a bed.
Let see it works!!!
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Conclusions
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References:• [LM] Bryan C. Russell and Antonio Torralba and Kevin P. Murphy and
William T. Freeman}, Labelme: A database and web-based tool for image annotation, MIT AI Lab Memo, 2005
• [DBP] Christian Bizer, Jens Lehmann, Georgi Kobilarov, Sören Auer, Christian Becker, Richard Cyganiak, Sebastian Hellmann: DBpedia – A Crystallization Point for the We of Data. Journal of Web Semantics: Science, Services and Agents on the World Wide Web, Issue 7, Pages 154–165, 2009.
• [TRA] Mihalcea, R., and Tarau, P. 2004. TextRank: Bringing order into texts. In Proc. Conf. Empirical Methods in Natural Language Processing, 404–411
• [CAPS] Ken Xu and James Stewart and Eugene Fiume , Constraint-Based Automatic Placement for Scene Composition, Proc. Graphics Interface, 2002,May, Calgary, Alberta, pp 25--34[ADW] Myers, P., R. Espinosa, C. S. Parr, T. Jones, G. S. Hammond, and T. A. Dewey. 2006. The Animal Diversity Web (online). Accessed November 01, 2011 at http://animaldiversity.org