skip to main content
10.1145/2362456.2362477acmotherconferencesArticle/Chapter ViewAbstractPublication Pagesi-knowConference Proceedingsconference-collections
research-article

Patent images - a glass-encased tool: opening the case

Authors Info & Claims
Published:05 September 2012Publication History

ABSTRACT

The paper discusses the problem of patent image retrieval. It describes the issues faced when extracting semantic data of images in patents, as well as an integration framework between the data thus extracted and semantic information extracted from text. Combining the two sources of knowledge is on the wish list of many patent information users, as current systems either search only the textual data, or have extremely limited image processing functionality. In practice in the patent domain, depictions of the product or method are often vital to the understanding of the invention. Yet they are almost completely unsearchable. They are tools enclosed in a glass case, at which we can look, but of which we cannot really make use. The IMPEx Project (Image Mining for Patent Exploration) cracks open this case with a new focus on processing this particular type of images. This paper presents the motivations, status and aims of the project.

References

  1. D. Alberts, C. B. Yang, D. Fobare-DePonio, K. Koubek, S. Robins, M. Rodgers, E. Simmons, and D. DeMarco. Current Challenges in Patent Information Retrieval, chapter 1: Introduction to Patent Searching - Practical Experience and Requirements for Searching the Patent Space. Springer Verlag, 2011.Google ScholarGoogle Scholar
  2. J. M. Barnard and G. M. Downs. Use of markush structure techniques to avoid enumeration in diversity analysis of large combinatorial libraries. http://www.daylight.com/meetings/mug97/Barnard/970227JB.html, (visited 03/2012) 1997.Google ScholarGoogle Scholar
  3. J. M. Barnard and P. M. Wright. Towards in-house searching of Markush structures from patents. World Patent Information, 31(2), 2009.Google ScholarGoogle Scholar
  4. D. Conte, P. Foggia, C. Sansone, and M. Vento. Thirty years of graph matching in pattern recognition. International Journal of Pattern Recognition and Artificial Intelligence, 18(4), 2004.Google ScholarGoogle Scholar
  5. Fairview Research. Alexandria patent data warehouse. http://www.intellogist.com/wiki/Alexandria, 2011.Google ScholarGoogle Scholar
  6. U. Garain and B. Chaudhuri. A corpus for ocr research on mathematical expressions. Int. J. Doc. Anal. Recognit., 7(4):241--259, Sept. 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. A. Hanbury, N. Bhatti, M. Lupu, and R. Mörzinger. Patent image retrieval: A survey. In Proc. of PaIR, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. R. M. Haralick and L. G. Shapiro. Computer and Robot Vision. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1st edition, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. B. Huet, G. Guarascio, N. J. Kern, and B. Mérialdo. Relational skeletons for retrieval in patent drawings. In ICIP (2), pages 737--740, 2001.Google ScholarGoogle Scholar
  10. A. Leach and V. Gillet. An Introduction to Chemoinformatics. Springer, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. L. Li and C. L. Tan. Associating figures with descriptions for patent documents. In Proc. of DAS, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. Lupu, J. Huang, J. Zhu, and J. Tait. TREC Chemical Information Retrieval - An Evaluation Effort for Chemical IR Systems. WPI Journal, 2011.Google ScholarGoogle Scholar
  13. M. Lupu, Z. Jiashu, J. Huang, H. Gurulingappa, I. Filipov, and J. Tait. Overview of the trec 2011 chemical ir track. In Proc. of TREC, 2011.Google ScholarGoogle Scholar
  14. R. Mörzinger, A. Horti, G. Thallinger, N. Bhatti, and A. Hanbury. Classifying patent images. In CLEF (Notebook Papers/Labs/Workshop), 2011.Google ScholarGoogle Scholar
  15. F. Piroi, M. Lupu, A. Hanbury, and V. Zenz. Clef-ip 2011: Retrieval in the intellectual property domain. In CLEF (Notebook Papers/Labs/Workshop), 2011.Google ScholarGoogle Scholar
  16. K. Riesen, X. Jiang, and H. Bunke. Exact and Inexact Graph Matching: Methodology and Applications. In C. C. Aggarwal and H. Wang, editors, Managing and Mining Graph Data, volume 40 of Advances in Database Systems. Springer, 2010.Google ScholarGoogle Scholar
  17. N. M. Sadawi, A. P. Sexton, and V. Sorge. Performance of MolRec at TREC 2011 Overview and Analysis of Results. In Proc. of TREC, 2011.Google ScholarGoogle Scholar
  18. P. Sidiropoulos, S. Vrochidis, and I. Kompatsiaris. Content-based binary image retrieval using the adaptive hierarchical density histogram. Pattern Recognition, 44(4):739--750, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. V. Smolov, F. Zentsev, and M. Rybalkin. Imago: open-source toolkit for 2D chemical structure image recognition. In Proc. of TREC, 2011.Google ScholarGoogle Scholar
  20. A. Tiwari and V. Bansal. Patseek: Content based image retrieval system for patent database. In ICEB, pages 1167--1171, 2004.Google ScholarGoogle Scholar
  21. S. Vrochidis, S. Papadopoulos, A. Moumtzidou, P. Sidiropoulos, E. Pianta, and I. Kompatsiaris. Towards content-based patent image retrieval: A framework perspective. World Patent Information, 32(2):94--106, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  22. M. Zimmermann. Chemical structure reconstruction with chemocr. In Proc. of TREC, 2011.Google ScholarGoogle Scholar

Index Terms

  1. Patent images - a glass-encased tool: opening the case

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Other conferences
            i-KNOW '12: Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies
            September 2012
            244 pages
            ISBN:9781450312424
            DOI:10.1145/2362456

            Copyright © 2012 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 5 September 2012

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article

            Acceptance Rates

            Overall Acceptance Rate77of238submissions,32%

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader