Advertisement

Image Abstraction in Crossmedia Retrieval for Text Illustration

  • Filipe Coelho
  • Cristina Ribeiro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7224)

Abstract

Text illustration is a multimedia retrieval task that consists in finding suitable images to illustrate text fragments such as blog entries, news reports or children stories. In this paper we describe a crossmedia retrieval system which, given a textual input, selects a short list of candidate images from a large media collection. This approach makes use of a recently proposed method to map metadata and visual features into a common textual representation that can be handled by traditional information retrieval engines. Content-based analysis is enhanced by visual abstraction, namely the Anisotropic Kuwahara Filter, which impacts feature information captured by the Joint Composite and Speeded Up Robust Features visual descriptors. For evaluation purposes, we used the well-established MIRFlickr photo collection, with 25,000 photos and user tags collected from Flickr as well as manual annotations provided as image retrieval groundtruth. Results show that image abstraction can improve visual retrieval as well as significantly reduce processing and storage requirements, even more when paired with Google’s WebP image format. We conclude that applying a visual rerank after an initial text retrieval step improves the quality of results, and that the adopted text mapping method for visual descriptors provides an effective crossmedia approach for text illustration.

Keywords

crossmedia retrieval image abstraction text illustration large-scale collections performance evaluation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-based Multimedia Information Retrieval: State of the Art and Challenges. ACM Transactions on Multimedia Computing, Communications, and Applications 2(1), 1–19 (2006)CrossRefGoogle Scholar
  2. 2.
    Liu, Y., Zhang, D., Lu, G., Ma, W.Y.: A Survey of Content-based Image Retrieval with High-level Semantics. Pattern Recognition 40(1), 262–282 (2007)zbMATHCrossRefGoogle Scholar
  3. 3.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image Retrieval: Ideas, Influences, and Trends of the New Age. ACM Computing Surveys (CSUR) 40(2), 1–60 (2008)CrossRefGoogle Scholar
  4. 4.
    Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence (2000)Google Scholar
  5. 5.
    Joshi, D., Wang, J.Z., Li, J.: The Story Picturing Engine: Finding Elite Images to Illustrate a Story Using Mutual Reinforcement. In: Proceedings of the 6th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp. 119–126. ACM, New York (2004)CrossRefGoogle Scholar
  6. 6.
    Joshi, D., Wang, J.Z., Li, J.: The Story Picturing Engine—a System for Automatic Text Illustration. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) 2(1), 89 (2006)Google Scholar
  7. 7.
    Delgado, D., Magalhães, J., Correia, N.: Assisted news reading with automated illustration. In: Proceedings of the International Conference on Multimedia, MM 2010, pp. 1647–1650. ACM, New York (2010)CrossRefGoogle Scholar
  8. 8.
    Coelho, F., Ribeiro, C.: Automatic Illustration with Cross-media Retrieval in Large-scale Collections. In: Proceedings of the 9th International Workshop on Content-based Multimedia Indexing (2011)Google Scholar
  9. 9.
    Coelho, F., Ribeiro, C.: Dpikt: Automatic Illustration System for Media Content. In: Proceedings of the 9th International Workshop on Content-Based Multimedia Indexing (2011)Google Scholar
  10. 10.
    Chatzichristofis, S.A., Arampatzis, A., Boutalis, Y.S.: Investigating the Behavior of Compact Composite Descriptors in Early Fusion, Late Fusion and Distributed Image Retrieval. Radioengineering 19(4), 725 (2010)Google Scholar
  11. 11.
    Coelho, F., Ribeiro, C.: Evaluation of Global Descriptors for Multimedia Retrieval in Medical Applications. In: Proceedings of the 4th International Workshop on Management and Interaction with Multimodal Information Content. IEEE Computer Society (2010)Google Scholar
  12. 12.
    Deselaers, T., Keysers, D., Ney, H.: Features for Image Retrieval: An Experimental Comparison. Information Retrieval 11(2), 77–107 (2008)CrossRefGoogle Scholar
  13. 13.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Evans, C.: Notes on the OpenSURF Library. Technical Report CSTR-09-001, University of Bristol (January 2009)Google Scholar
  15. 15.
    Kyprianidis, J.E., Kang, H., Döllner, J.: Image and Video Abstraction by Anisotropic Kuwahara Filtering. Computer Graphics Forum 28(7), 1955–1963 (2009); Special issue on Pacific Graphics 2009CrossRefGoogle Scholar
  16. 16.
    Kyprianidis, J.E., Kang, H., Döllner, J.: Anisotropic Kuwahara Filtering on the GPU. In: Engel, W. (ed.) GPU Pro - Advanced Rendering Techniques, pp. 247–264 (2010)Google Scholar
  17. 17.
    Gennaro, C., Amato, G., Bolettieri, P., Savino, P.: An Approach to Content-Based Image Retrieval Based on the Lucene Search Engine Library. In: Lalmas, M., Jose, J., Rauber, A., Sebastiani, F., Frommholz, I. (eds.) ECDL 2010. LNCS, vol. 6273, pp. 55–66. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  18. 18.
    Amato, G., Bolettieri, P., Falchi, F., Gennaro, C., Rabitti, F.: Combining Local and Global Visual Feature Similarity Using a Text Search Engine. In: Proceedings of the 9th International Workshop on Content-based Multimedia Indexing, pp. 49–54. IEEE (2011)Google Scholar
  19. 19.
    Amato, G., Savino, P.: Approximate Similarity Search in Metric Spaces Using Inverted Files. In: Proceedings of the 3rd International Conference on Scalable Information Systems, p. 28. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) (2008)Google Scholar
  20. 20.
    Esuli, A.: PP-Index: Using Permutation Prefixes for Efficient and Scalable Approximate Similarity Search. In: Proceedings of the 7th Workshop on Large-Scale and Distributed Systems for Information Retrieval (2009) ISSN: 1613–0073Google Scholar
  21. 21.
    Arthur, D., Vassilvitskii, S.: k-means++: The Advantages of Careful Seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)Google Scholar
  22. 22.
    Huiskes, M.J., Lew, M.S.: The MIR Flickr Retrieval Evaluation. In: MIR 2008: Proceedings of the 2008 ACM International Conference on Multimedia Information Retrieval. ACM, New York (2008)Google Scholar
  23. 23.
    Huiskes, M.J., Thomee, B., Lew, M.S.: New Trends and Ideas in Visual Concept Detection: The MIR Flickr Retrieval Evaluation Initiative. In: Proceedings of the International Conference on Multimedia Information Retrieval, pp. 527–536. ACM (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Filipe Coelho
    • 1
    • 2
  • Cristina Ribeiro
    • 1
    • 2
  1. 1.INESC Technology and SciencePortoPortugal
  2. 2.Department of Informatics EngineeringUniversity of PortoPortoPortugal

Personalised recommendations