skip to main content
10.1145/951676.951690acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
Article

Robust content-based image searches for copyright protection

Published:07 November 2003Publication History

ABSTRACT

This paper proposes a novel content-based image retrieval scheme for image copy identification. Its goal is to detect matches between a set of doubtful images and the ones stored in the database of the legal holders of the photographies. If an image was stolen and used to create a pirated copy, it tries to identify from which original image that copy was created. The image recognition scheme is based on local differential descriptors. Therefore, the matching process takes into account a large set of variations that might have been applied to stolen images in order to create pirated copies. The high cost and the complexity of this image recognition scheme requires a very efficient retrieval process since many individual queries must be executed before being able to construct the final result. This paper therefore proposes to use a novel search method that trades the precision of each individual search for reduced query execution time. This imprecision has only little impact on the overall recognition performance since the final result is a consolidation of many partial results. However, it dramatically accelerates queries. This result has then been corroborated by a theoretically study. Experiments show the efficiency and the robustness of the proposed scheme.

References

  1. L. Amsaleg and P. Gros. Content-based retrieval using local descriptors: Problems and issues from a database perspective. Pattern Analysis and Applications, Special Issue on Image Indexation, 4:108--124, 2001.]]Google ScholarGoogle Scholar
  2. L. Amsaleg, P. Gros, and S.-A. Berrani. Robust object recognition in images and the related database problems. Special issue of the Journal of Multimedia Tools and Applications, 2003 (To appear).]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. M. Basseville. Detecting changes in signals and systems -- A survey. Automatica, 24(3):309--326, 1988.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. K. P. Bennett, U. Fayyad, and D. Geiger. Density-based indexing for approximate nearest-neighbor queries. Proceedings of the 5th ACM International Conference on Knowledge Discovery and Data Mining, San Diego, California, USA, pages 233--243, August 1999.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. S.-A. Berrani, L. Amsaleg, and P. Gros. Approximate searches: k-neighbors+precision. In Proceedings of the 12th acm International Conference on Information and Knowledge Management, New Orleans, Louisiana, USA, November 2003.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. S.-A. Berrani, L. Amsaleg, and P. Gros. Probabilistically controlling the precision of approximate nearest-neighbor searches. In 19e journées de Bases de Données Avancées (BDA'03), Lyon, France, October 2003.]]Google ScholarGoogle Scholar
  7. K. S. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft. When is "nearest neighbor" meaningful? In Proceedings of the 7th International Conference on Database Theory, Jerusalem, Israel, pages 217--235. Springer, January 1999.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Y. Dufournaud, C. Schmid, and R. Horaud. Matching images with different resolutions. In Proceedings of the Conference on Computer Vision and Pattern Recognition, Hilton Head Island, South Carolina, USA, volume 1, pages 612--618, June 2000.]]Google ScholarGoogle ScholarCross RefCross Ref
  9. L. Florack, B. ter Haar Romeny, J. Koenderink, and M. Viergever. General intensity transformation and differential invariants. Journal of Mathematical Imaging and Vision, 4(2):171--187, 1994.]]Google ScholarGoogle ScholarCross RefCross Ref
  10. C. G. Harris and M. J. Stephens. A combined corner and edge detector. In Proceedings of the 4th Alvey Vision Conference, Manchester, England, pages 147--151, September 1988.]]Google ScholarGoogle ScholarCross RefCross Ref
  11. D. Hinkley. Inference about the change-point from cumulative sum tests. Biometrika, 58:509--523, 1971.]]Google ScholarGoogle ScholarCross RefCross Ref
  12. P. Indyk and R. Motwani. Approximate nearest neighbors: towards removing the curse of dimensionality. In Proceedings of 13th Annual acm Symposium on Theory of Computing, Dallas, Texas, USA, pages 604--613, May 1998.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. E. Page. Continous inspection schemes. Biometrika, 41:100--115, 1954.]]Google ScholarGoogle ScholarCross RefCross Ref
  14. C. V. Rijsbergen. Information Retrieval. Butterworth, 1979.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. C. Schmid and R. Mohr. Local grayvalue invariants for image retrieval. sc ieee Transactions on Pattern Analysis and Machine Intelligence, 19(5):530--534, 1997.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. R. C. Veltkamp and M. Tanase. Content-based image retrieval systems: A survey. Technical Report UU-CS-2000-34, Department of Computing Science, Utrecht University, October 2000.]]Google ScholarGoogle Scholar
  17. R. Weber, H.-J. Schek, and S. Blott. A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In Proceedings of the 24th International Conference on Very Large Data Bases, New York City, New York, USA, pages 194--205, August 1998.]] Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Robust content-based image searches for copyright protection

      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 Conferences
        MMDB '03: Proceedings of the 1st ACM international workshop on Multimedia databases
        November 2003
        102 pages
        ISBN:1581137265
        DOI:10.1145/951676

        Copyright © 2003 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: 7 November 2003

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • Article

        Upcoming Conference

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader