NLPainter “Text Analysis for picture/movie generation” David Leoni Eduardo C á rdenas 12/01/2012

2 M OTIVATION 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 H OW 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.

4 H OW 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.

5 D ATABASES 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 pages were information about animals 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 LabelMe D ATABASES

7 Animal Diversity D ATABASES

8 General Diagram :

9 Specific Diagram ( Text ): 9

10 Specific Diagram ( Images ): 10

Specific Diagram ( Ontology ): 11

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

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

Technologies and algorithms (Ontology) 14 Editor:  Protégé 4.1 RDF engine:  OWLim Lite Upper ontology:  Wordnet 14

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

Technologies and algorithms (General project) 16 Documentation:  Google Wiki Versioning:  SVN Project Web Page:  16

How to run the project? 17

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

Our Project working! 19

Some Results: 20

Some Results: 21

Some Results: 22

Some Results: 23 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

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, [TRA] Mihalcea, R., and Tarau, P 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 [ADW] Myers, P., R. Espinosa, C. S. Parr, T. Jones, G. S. Hammond, and T. A. Dewey The Animal Diversity Web (online). Accessed November 01, 2011 at 25