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NLPainter “Text Analysis for picture/movie generation”

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.

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NLPainter “Text Analysis for picture/movie generation”

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  1. NLPainter “Text Analysis for picture/movie generation” David Leoni Eduardo Cárdenas 12/01/2012

  2. Motivation for choosing the project: • The purpose of our project is to transform text in images trying that both express the same mining. • More than 50% of used human brain is devoted to vision. • 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.

  3. 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 withtags with the things that are inside of them. We selected the most representative picture in our database that describes a specific object.

  4. How the problem can be solve? We used some text mining techniques in order to obtain entities, attributes, etc. We usedthe PoS of the phrase that we want to convert to image. We associated the text with the images.

  5. Databases The following databases of images was used forour 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

  6. Databases • LabelMe

  7. Databases • Animal Diversity

  8. General Diagram:

  9. Specific Diagram (Text): 9

  10. Specific Diagram (Images): 10

  11. Specific Diagram (Ontology): 11

  12. Technologies and algorithms(Text) • Programming Environment: • Netbeans • Packages: • Stanford Parser • Additional Packages: • Image Generator 12

  13. Technologies and algorithms(Image) • Programmation Language: • MATLAB, Java • Programming Environment: • Netbeans • Packages: • LabelMe • XOM 13

  14. Technologies and algorithms(Ontology) • Editor: • Protégé 4.1 • RDF engine: • OWLimLite • Upper ontology: • Wordnet 14

  15. Technologies and algorithms(General project) • Programming Environment: • NetBeans • RDF engine: • OWLIM lite • Packages: • XOM • Web server: • Apache Tomcat 7.0 • JSP 15

  16. Technologies and algorithms(General project) • Documentation: • Google Wiki • Versioning: • SVN • Project Web Page: • http://code.google.com/p/nlpainter/ 16

  17. How to run the project? 17

  18. Comparison with other results: • The Story Picturing Engine • A Text-to-Picture Synthesis System for Augmenting Communication • WordsEye

  19. Our Project working! 19

  20. Some Results:

  21. Some Results:

  22. Some Results:

  23. 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. Let see it works!!!

  24. Conclusions

  25. 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, GeorgiKobilarov, 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

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