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Journal on Data Semantics VII pp 196–218Cite as

Automatic Image Description Based on Textual Data

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Part of the book series: Lecture Notes in Computer Science ((JODS,volume 4244))

Abstract

In the last two decades, images are quite produced in increasing amounts in several application domains. In medicine, for instance, a large number of images of various imaging modalities (e.g. computer tomography, magnetic resonance, nuclear imaging, etc.) are produced daily to support clinical decision-making. Thereby, a fully functional Image Management System becomes a requirement to the end-users. In spite of current researches, the practice has proved that the problem of image management is highly related to image representation. This paper contribution is twofold in facilitating the representation of images and the extraction of its content and context descriptors. In fact, we introduce an expressiveness and extendable XML-based meta-model able to capture the metadata and content-based of images. We also propose an information extraction approach to provide automatic description of image content using related metadata. It automatically generates XML instances, which mark up metadata and salient objects matched by extraction patterns. In this paper, we illustrate our proposal by using the medical domain of lungs x-rays and we show our first experimental results.

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References

  1. Wu, J.K., Narasimhalu, A.D., Mehtre, B.M., Lam, C.P., Gao, Y.J.: CORE: A Content-Based Retrieval Engine for Multimedia Information Systems. Multimedia Systems 3, 25–41 (1995)

    Article  Google Scholar 

  2. Berchtold, S., Boehm, C., Braunmueller, B., et al.: Fast Parallel Similarity Search in Multimedia Databases. In: SIGMOD Conference, AZ, USA, pp. 1–12 (1997)

    Google Scholar 

  3. Yoshitaka, A., Ichikawa, T.: A Survey on Content-Based Retrieval for Multimedia Databases. IEEE Transactions on Knowledge and Data Engineering 11(1), 81–93 (1999)

    Article  Google Scholar 

  4. Oria, V., Özsu, M.T., Liu, L., et al.: Modeling Images for Content-Based Queries: The DISMA Approach. In: VIS 1997, San Diago, pp. 339–346 (1997)

    Google Scholar 

  5. Wu, J.K.: Content-Based Indexing of Multimedia Databases. IEEE TKDE 9(6), 978–989 (1997)

    Google Scholar 

  6. Rui, Y., Huang, T.S., Chang, S.F.: Image Retrieval: Past, Present, and Future. Journal of Visual Communication and Image Representation 10, 1–23 (1999)

    Article  Google Scholar 

  7. Stonebraker, M., Brown, P.: Object-Relational DBMSs. Mogan Kaufmann Pub. Inc., San Francisco (1999)

    Google Scholar 

  8. Excalibur Image Datablade Module User’s Guide. Informix Press (March 1999) Ver. 1.2, P. No. 000-5356

    Google Scholar 

  9. Oracle8i, Visual Information Retrieval Users Guide & Reference. Oracle Press (1999) Release 8.1.5, A67293-01

    Google Scholar 

  10. Grosky, W.I.: Managing Multimedia Information in Database Systems. Communications of the ACM 40(12), 72–80 (1997)

    Article  Google Scholar 

  11. Grosky, W.I., Stanchev, P.L.: An Image Data Model. In: Laurini, R. (ed.) VISUAL 2000. LNCS, vol. 1929, pp. 14–25. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  12. Eakins, J.P., Graham, M.E.: Content-Based Image Retrieval: A Report to the JISC Technology Applications Programme. Inst. for Image Data Research, Univ. of North-umbria at Newcastle (January 1999)

    Google Scholar 

  13. Smeulders, A.W.M., Gevers, T., Kersten, M.L.: Crossing the Divide Between Computer Vision and Databases in Search of Image Databases. In: Visual Database Systems Conf., Italy, pp. 223–239 (1998)

    Google Scholar 

  14. Sheth, A., Klas, W.: Multimedia Data Management: Using Metadata to Integrate and Apply Digital Media. McGraw-Hill, San Francisco (1998)

    Google Scholar 

  15. Badr, Y.: Xtractor: A Light Wrapper For XML Paragraph-Centric Documents. In: Proceedings of the 2005 International Conference on Signal-Image Technology & Internet - Based Systems (IEEE - SITIS 2005), Yaoundé Cameroon, pp. 150–155 (2005)

    Google Scholar 

  16. Veltkamp, R.C., Tanase, M.: Content-Based Image Retrieval Systems: A Survey, Technical Report UU-cs-2000-34, Department of Computer Science, Utrecht University (October 2000)

    Google Scholar 

  17. Oria, V., Özsu, M.T., Iglinski, P., et al.: DISMA: An Object Oriented Approach to Developing an Image Database System, ICDE 2000. In: 16th Int. Conf. on Data Engineering, San Diego, California (February 2000)

    Google Scholar 

  18. Oria, V., Özsu, M.T., Iglinski, P., et al.: DISMA: A Distributed and Interoperable Image Database System. In: Proc. of ACM SIGMOD Int. Conf. on Management of Data, SIGMOD 2000, Dallas, Texas (2000)

    Google Scholar 

  19. Duncan, J.S., Ayache, N.: Medical Image Analysis: Progress over Two Decades and the Challenges Ahead. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1) (January 2000)

    Google Scholar 

  20. Soderland, S., Fisher, D., Aseltine, J., et al.: Issues in inductive learning of domain-specic text extraction rules. In: Learning for Natural Language Processing, pp. 290–301. Springer, Heidelberg (1996)

    Google Scholar 

  21. Allen, J.E.: Maintaining Knowledge about Temporal Intervals. Communications of ACM 26, 832–843 (1983)

    Article  MATH  Google Scholar 

  22. Chbeir, R., Favetta, F.: A Global Description of Medical Image with a High Precision. In: IEEE International Symposium on Bio-Informatics and Biomedical Engineering IEEE-BIBE 2000, Washington D.C., USA, November 8th-10th, pp. 289–296. IEEE Computer Society, Los Alamitos (2000)

    Chapter  Google Scholar 

  23. Chu, W.W., Hsu, C.C., Cárdenas, A.F., et al.: Knowledge-Based Image Retrieval with Spatial and Temporal Constraints. IEEE Transactions on Knowledge and Data Engineering 10(6), 872–888 (1998)

    Article  Google Scholar 

  24. Mechkour, M.: EMIR2. An Extended Model for Image Representation and Retrieval. In: Database and Expert system Applications (DEXA), pp. 395–404 (September 1995)

    Google Scholar 

  25. Trayser, G.: Interactive System for Image Selection, Digital Imaging Unit Center of Medical Informatics University Hospital of Geneva, http://www.expasy.ch/UIN/html1/projects/isis/isis.html

  26. Narasimhalu, A.D.: Multimedia Databases, Multimedia Systems, vol. 4, pp. 226–249. Springer, Heidelberg (1996)

    Google Scholar 

  27. Lu, G.: Multimedia Database Management Systems. Artech House Computing library (1999) ISBN 0-089006-342-7

    Google Scholar 

  28. Hopcroft, J.E., Ullman, J.D.: Introduction to automata theory languages, and computation. Addison-Wesley Publishing Co., Reading (1979)

    MATH  Google Scholar 

  29. Hume, A.: A tale of two greps. Software Practice and Experience 18(11), 1063–1072 (1988)

    Article  Google Scholar 

  30. Wall, L., Christensen, T., Schwartz, R.L.: Programming Perl, 2nd edn. O’Reilly & Associates, Inc., Sebastopol (1996)

    MATH  Google Scholar 

  31. Smith, D.J., Lopez, M.: Information extraction for semi-structured documents. In: Proc. Workshop on Management of Semistructured Data (May 1997)

    Google Scholar 

  32. Hammer, J., Garcia-Molina, H., Cho, J., et al.: Extracting Semi structured Information from the Web. In: Proceedings of the Workshop on Management of Semistructured Data, Tucson, Arizona (May 1997)

    Google Scholar 

  33. Hsu, C.N., Dung, M.T.: Generating finite-state transducers for semistructured data extraction from the web. Information Systems, Special Issue on Semistructured Data 23(8), 521–538 (1998)

    Google Scholar 

  34. Ashish, N., Knoblock, C.: Wrapper Generation for Semi-structured Internet Sources. In: ACM SIGMOD Workshop on Management of Semistructured Data, Tucson, Arizona (1997)

    Google Scholar 

  35. Kuhlins, S., Tredwell, R.: Toolkits for Generating Wrappers: A survey. In: Aksit, M., Mezini, M., Unland, R. (eds.) NODe 2002. LNCS, vol. 2591. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  36. Sankar, S., Viswanadha, S., Duncan, R.: Java Compiler Compiler (JavaCC)

    Google Scholar 

  37. The Java Parser Generator. Located at: http://www.suntest.com/JavaCC/

  38. Savarese, D.F.: OROmatcher - Regular Expressions for Java, http://www.savarese.org/

  39. Karttunen, L., Chanod, J.-P., Grefenstette, G., Schiller, A.: Regular expressions for language engineering. Journal of national language engineering 2(4), 305–328 (1996)

    Article  Google Scholar 

  40. van Noord, G., Gerdemann, D.: An Extendible Regular Expression Compiler for Finite-State Approaches in Natural Language Processing. In: Boldt, O., Jürgensen, H. (eds.) WIA 1999. LNCS, vol. 2214, p. 122. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  41. MPEG-7 Overview (visited at, 26/02/2006), http://www.chiariglione.org/MPEG/standards/mpeg-7/mpeg-7.htm

  42. Chang, S.K., Shi, Q.Y., Yan, C.W.: Iconic Indexing by 2-D Strings. IEEE-Transactions-on-Pattern-Analysis-and-Machine-Intelligence PAMI-9(3), 413–428 (1987)

    Article  Google Scholar 

  43. Chang, S.K., Jungert, E.: Human- and System-Directed Fusion of Multimedia and Multimodal Information using the Sigma-Tree Data Model. In: Leung, C. (ed.) Visual Information Systems. LNCS, vol. 1306, pp. 21–28. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  44. Huang, P.W., Jean, Y.R.: Using 2D C+-Strings as spatial knowledge representation for image database management systems. Pattern Recognition 27(9), 1249–1257 (1994)

    Article  Google Scholar 

  45. Egenhofer, M.: Query Processing in Spatial Query By Sketch. Journal of Visual Language and Computing 8(4), 403–424 (1997)

    Article  Google Scholar 

  46. El-kwae, M.A., Kabuka, M.R.: A robust framework for Content-Based Retrieval by Spatial Similarity in Image Databases. ACM Transactions on Information Systems 17(2), 174–198 (1999)

    Article  Google Scholar 

  47. Peuquet, D.J.: The use of spatial relationships to aid spatial database retrieval. In: Proc. Second Int. Symp. on Spatial Data Handling, Seattle, pp. 459–471 (1986)

    Google Scholar 

  48. Egenhofer, M., Frank, A., Jackson, J.: A Topological Data Model for Spatial Databases. In: Buchmann, A., Smith, T.R., Wang, Y.-F., Günther, O. (eds.) SSD 1989. LNCS, vol. 409, pp. 271–286. Springer, Heidelberg (1990)

    Google Scholar 

  49. Gross, M.: The Use of Finite Automata in the Lexical Representation of Natural Language. In: Gross, M., Perrin, D. (eds.) LITP 1987. LNCS, vol. 377, pp. 34–50. Springer, Heidelberg (1989)

    Google Scholar 

  50. Courtois, B.: Le dictionnaire electronique des mots simples. In: Les dctionnaires electroniques. Langue francaise no 87. Larousse, Paris (1990)

    Google Scholar 

  51. Silberztein, M.: INTEX: a Finite State Transducer toolbox. In: Theoretical Computer Science #231:1. Elsevier Science, Amsterdam (1999)

    Google Scholar 

  52. Subramaniam, L.V., Mukherjea, S., Kankar, P., Srivastava, B., Batra, V.S., Kamesam, P.V., Kothari, R.: Information Extraction from Biomedical Literature: Methodology, Evaluation and an Application, IBM India Research Lab, New Delhi, India

    Google Scholar 

  53. Fukuda, K., Tsunoda, T., Tamura, A., Takagi, T.: Toward Information Extraction: Identify-ing Protein Names from Biological Papers. In: Proceedings of the Pacific Symposium on Biocomputing, Hawaii, pp. 707–718 (1998)

    Google Scholar 

  54. Daniel, Q., Hesham, A.: Ontology Specific Data Mining Based on Dynamic Grammars. In: Bioinformatics conference, Stanford, CA, August 16-19 (2004)

    Google Scholar 

  55. Embley, D.W., Campbell, D.M., Smith, R.D.: Ontology-Based Extraction and Structuring of Information from Data-Rich Unstructured Documents. In: Proceedings of CIKM 1998, Bethesda, Maryland (1998)

    Google Scholar 

  56. Bricon-Souf, N., Beuscart-Zéphir, M.C., Watbled, L., Laforest, F., Karadimas, H., Anceaux, F., Flory, A., Lepage, E., Beuscart, R.: Technologies de l’Information Pour l’Hospitalisation A Domicile: le projet TIPHAD, Télémédecine et e-Santé, Collection Infor-matique et Santé, Paris, vol. 13. Springer- Verlag (2002)

    Google Scholar 

  57. Unitex Home page (last visited, March 12th 2006), Available at: http://www-igm.univ-mlv.fr/~unitex/

  58. Frakes, W.B., Baeza-Yates, R.: Information Retrieval: Data Structures & Algorithms. Prentice Hall, Englewood Cliffs (1992)

    Google Scholar 

  59. Appelt, D.E., Israel, D.J.: Introduction to Information Extraction Technology. In: Tutorial for IJCAI 1999, Stockholm (1999)

    Google Scholar 

  60. Charniak, E.: Statistical Language Learning, p. 192. MIT Press, Cambridge (1994)

    Google Scholar 

  61. Brill, E., Church, K.: Proceedings of the Conference on Empirical Methods in Natural Language Processing. University of Pennsylvania. Philadelphia, PA (1996)

    Google Scholar 

  62. Marcus, M., Santorini, B., Marcinkiewicz, M.: Building a large annotated corpus of English. Computational Linguistics 19(2), 313–330 (1993)

    Google Scholar 

  63. Freitag, D., McCallum, A.: Information extraction with HMMs and shrinkage. In: Proceedings of the AAAI 1999 Workshop on Machine Learning for Information Extraction, pp. 31–36 (1999)

    Google Scholar 

  64. Miikkulainen, R.: Subsymbolic Natural Language Processing: An Integrated Model of Scripts, Lexicon, and Memory. MIT Press, Cambridge (1993)

    Google Scholar 

  65. Brill, E.: Transformation-based error-driven learning and natural language processing: A case study in part-of-speech tagging. Computational Linguistics 21(4), 543–565 (1995)

    Google Scholar 

  66. Magerman, D.M.: Statistical decision-tree models for parsing. In: Proceedings of the 33rd Annual Meeting of the Association for Computational Lenguistics, Cambridge, pp. 276–283 (1995)

    Google Scholar 

  67. Wermter, S., Rilo, E., Scheler, G.: Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing, pp. 315–328. Springer, Berlin (1996)

    Google Scholar 

  68. Lavrac, N., Dzeroski, S.: Inductive Logic Programming: Techniques and Applications. Ellis Horwood (1994)

    Google Scholar 

  69. Huffman, S.: Learning information extraction patterns from examples. In: Workshop on Learning for Natural Language Processing, IJCAI 1995, Canada, pp. 246–260 (1995)

    Google Scholar 

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Author information

Authors and Affiliations

  1. PRISMa – INSA de Lyon, 20 Av. Einstein, F-69621, Villeurbanne, France

    Youakim Badr

  2. LE2I – Bourgogne University, BP 47870, 21078 Cedex, Dijon, France

    Richard Chbeir

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  1. Youakim Badr
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  2. Richard Chbeir
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  1. EPFL-IC-IIF-LBD, Station 14 - INJ 236, 1015, Lausanne, Switzerland

    Stefano Spaccapietra

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Badr, Y., Chbeir, R. (2006). Automatic Image Description Based on Textual Data. In: Spaccapietra, S. (eds) Journal on Data Semantics VII. Lecture Notes in Computer Science, vol 4244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11890591_7

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  • DOI: https://doi.org/10.1007/11890591_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46329-0

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