Skip to main content

Enhancing Patent Search with Content-Based Image Retrieval

  • Chapter
Professional Search in the Modern World

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8830))

Abstract

Nowadays most of the patent search systems still rely upon text to provide retrieval functionalities. Recently, the intellectual property and information retrieval communities have shown great interest in patent image retrieval, which could augment the current practices of patent search. In this chapter, we present a patent image extraction and retrieval framework, which deals with patent image extraction and multimodal (textual and visual) metadata generation from patent images with a view to provide content-based search and concept-based retrieval functionalities. Patent image extraction builds upon page orientation detection and segmentation, while metadata extraction from images is based on the generation of low level visual and textual features. The content-based retrieval functionality is based on visual low level features, which have been devised to deal with complex black and white drawings. Extraction of concepts builds upon on a supervised machine learning framework realised with Support Vector Machines and a combination of visual and textual features. We evaluate the different retrieval parts of the framework by using a dataset from the footwear and the lithography domain.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. List, J.: How Drawings Could Enhance Retrieval in Mechanical and Device Patent Searching. World Patent Information 29, 210–218 (2007)

    Article  MathSciNet  Google Scholar 

  2. Adams, S.: Electronic non-text material in patent applications—some questions for patent offices, applicants and searchers. World Patent Information 27(2), 99–103 (2005)

    Article  Google Scholar 

  3. Zeng, Z., Zhao, J., Xu, B.: An Outward-Appearance Patent-Image Retrieval Approach Based on the Contour-Description Matrix. In: Proceedings of the 2007 Japan-China Joint Workshop on Frontier of Computer Science and Technology, pp. 86–99 (2007)

    Google Scholar 

  4. Codina, J., Pianta, E., Vrochidis, S., Papadopoulos, S.: Integration of Semantic, Metadata and Image Search Engines with a Text Search Engine for Patent Retrieval. In: Semantic Search 2008 Workshop, Tenerife, Spain (2008)

    Google Scholar 

  5. Vrochidis, S., Papadopoulos, S., Moumtzidou, A., Sidiropoulos, P., Pianta, E., Kompatsiaris, I.: Towards Content-based Patent Image Retrieval; A Framework Perspective. World Patent Information Journal 32(2), 94–106 (2010)

    Article  Google Scholar 

  6. Tiwari, A., Bansal, V.: PATSEEK: Content Based Image Retrieval System for Patent Database. In: Proceedings of the International Conference on Electronic Business 2004, Tsinghua University, Beijing, China (2004)

    Google Scholar 

  7. Eakins, J.P.: Trademark Image Retrieval. In: Springer-Verlag Principles of Visual Information Retrieval. Berlin (2001)

    Google Scholar 

  8. Jain, A.K., Vailaya, A.: Shape-based Retrieval: A case study with trademark image databases. Pattern Recognition 31, 1369–1390 (1998)

    Article  Google Scholar 

  9. Kim, Y.S., Kim, W.Y.: Content-based Trademark Retrieval System Using a Visually Salient Feature. Image and Vision Computing 16, 931–939 (1998)

    Article  Google Scholar 

  10. Wu, J.K., Lam, C.P., Mehtre, B.M., Gao, Y.J., Desai Narasimhalu, A.: Content-based Retrieval for Trademark Registration. Multimedia Tools and Applications 3, 245–267 (1996)

    Article  Google Scholar 

  11. Eakins, J.P., Boardman, J.M., Graham, M.E.: Similarity Retrieval of Trademark Images. IEEE Multimedia 5, 53–63 (1998)

    Article  Google Scholar 

  12. Alwis, S., Austin, J.: Trademark Image Retrieval Using Multiple Features. In: Proceedings of the 1999 International Conference on Challenge of Image Retrieval (IM 1999), Newcastle-upon-Tyne, U.K. (1999)

    Google Scholar 

  13. Schietse, J., Eakins, J.P., Veltkamp, R.C.: Practice and Challenges in Trademark Image Retrieval. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval (CIVR), pp. 518–524 (2007)

    Google Scholar 

  14. LTU Technologies, http://www.ltutech.com/en/

  15. eMARKS Project, http://emarks.iisa-innov.com/

  16. Huet, B., Kern, N.J., Guarascio, G., Merialdo, B.: Relational Skeletons for Retrieval In Patent Drawings. In: ICIP 2001, vol. 2, pp. 737–740 (2001)

    Google Scholar 

  17. Zeng, Z., Zhao, J., Xu, B.: An Outward-Appearance Patent-Image Retrieval Approach Based on the Contour-Description Matrix. In: Proceedings of the 2007 Japan-China Joint Workshop on Frontier of Computer Science and Technology, pp. 86-89 (2007)

    Google Scholar 

  18. PATExpert (FP6-028116), http://www.patexpert.org/

  19. Sidiropoulos, P., Vrochidis, S., Kompatsiaris, I.: Content-Based Binary Image Retrieval Using the Adaptive Hierarchical Density Histogram. Pattern Recognition Journal 44(4), 739–750 (2011)

    Article  Google Scholar 

  20. Ypma, G.: Evaluation of Patent Image Retrieval. In: Information Retrieval Facility Symposium 2010 (IRFS 2010), Vienna, Austria (2010)

    Google Scholar 

  21. Yan, R., Hsu, W.: Recent Developments in Content-based and Concept-based Image/Video Retrieval. In: Proceedings of the 16th ACM International Conference on Multimedia (MM 2008), New York, USA (2008)

    Google Scholar 

  22. Mörzinger, R., Horti, A., Thallinger, G., Bhatti, N., Hanbury, A.: Classifying Patent Images. In: Proceedings of CLEF 2011, Amsterdam (2011)

    Google Scholar 

  23. Csurka, G., Renders, J., Jacquet, G.: XRCE’s Participation at Patent Image Classification and Image-based Patent Retrieval Tasks of the Clef-IP 2011. In: Proceedings of CLEF 2011, Amsterdam (2011)

    Google Scholar 

  24. Vrochidis, S., Moumtzidou, A., Kompatsiaris, I.: Concept-based Patent Image Retrieval. World Patent Information Journal 34(4), 292–303 (2012)

    Article  Google Scholar 

  25. De Marco, D.: Mechanical Patent Searching: A Moving Target. In: Patent Information Users Group (PIUG), Baltimore, USA (2010)

    Google Scholar 

  26. Hoenes, F., Lichter, J.: Layout Extraction of Mixed Mode Documents. Mach. Vision Appl. 7, 237–246 (1994)

    Article  Google Scholar 

  27. Porter, M.F.: An Algorithm for Suffix Stripping. Program 14(3), 130–137 (1980)

    Article  Google Scholar 

  28. The Lemur Toolkit lemur, http://www.cs.cmu.edu/

  29. Boser, B.E., Guyon, I.M., Va, V.N.: A Training Algorithm for Optimal Margin Classifiers. In: Proceedings of the 5th Annual Workshop on Computational Learning Theory (COLT 1992), pp. 144–152. ACM Press, New York (1992)

    Chapter  Google Scholar 

  30. Chang, C., Lin, C.: LIBSVM: A Library for Support Vector Machines. Software available at: http://www.csie.ntu.edu.tw/~cjlin/libsvm

  31. Izquierdo, E., Casas, J., Leonardi, R., Migliorati, P., O’Connor, N., Kompatsiaris, I., Strintzis, M.G.: Advanced Content-Based Semantic Scene Analysis and Information Retrieval: The Schema Project. In: Proceedings Workshop on Image Analysis for Multimedia Interactive Services, London, UK, pp. 519–528 (2003)

    Google Scholar 

  32. Vrochidis, S., Moumtzidou, A., Ypma, G., Kompatsiaris, I.: PatMedia: Augmenting Patent Search with Content-based Image Retrieval. In: Proceedings of the 5th IRF Conference, Austria, Vienna (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Vrochidis, S., Moumtzidou, A., Kompatsiaris, I. (2014). Enhancing Patent Search with Content-Based Image Retrieval. In: Paltoglou, G., Loizides, F., Hansen, P. (eds) Professional Search in the Modern World. Lecture Notes in Computer Science, vol 8830. Springer, Cham. https://doi.org/10.1007/978-3-319-12511-4_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12511-4_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12510-7

  • Online ISBN: 978-3-319-12511-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics