Dating Historical Color Images

  • Frank Palermo
  • James Hays
  • Alexei A. Efros
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)

Abstract

We introduce the task of automatically estimating the age of historical color photographs. We suggest features which attempt to capture temporally discriminative information based on the evolution of color imaging processes over time and evaluate the performance of both these novel features and existing features commonly utilized in other problem domains on a novel historical image data set. For the challenging classification task of sorting historical color images into the decade during which they were photographed, we demonstrate significantly greater accuracy than that shown by untrained humans on the same data set. Additionally, we apply the concept of data-driven camera response function estimation to historical color imagery, demonstrating its relevance to both the age estimation task and the popular application of imitating the appearance of vintage color photography.

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References

  1. 1.
    Aviator visual effects - behind the scenes, http://www.aviatorvfx.com/
  2. 2.
    Alien Skin Software, LLC: Alien skin software: Exposure 4 examples, http://www.alienskin.com/exposure/exposure_examples.aspx
  3. 3.
    Amazon.com, Inc.: Amazon mechanical turk, http://www.mturk.com/
  4. 4.
    Athanasopoulos, A., Dimou, A., Mezaris, V., Kompatsiaris, I.: Gpu acceleration for support vector machines. In: Proceedings of the 12th International Workshop on Image Analysis for Multimedia Interactive Services, WIAMIS 2011 (2011)Google Scholar
  5. 5.
    Chakrabarti, A., Scharstein, D., Zickler, T.: An empirical camera model for internet color vision. In: Proceedings of the British Machine Vision Conference, BMVC (2009)Google Scholar
  6. 6.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
  7. 7.
    Dugad, R., Ratakonda, K., Ahuja, N.: Robust video shot change detection. In: IEEE Workshop on Multimedia Signal Processing (1998)Google Scholar
  8. 8.
    Haines, R.W.: Technicolor Movies: The History of Dye Transfer Printing. McFarland & Company, Inc. (1993)Google Scholar
  9. 9.
    Hays, J., Efros, A.A.: Scene completion using millions of photographs. ACM Transactions on Graphics (SIGGRAPH 2007) 26(3) (2007)Google Scholar
  10. 10.
    Hays, J., Efros, A.A.: im2gps: estimating geographic information from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2008)Google Scholar
  11. 11.
    Instagram, Inc.: Instagram, http://instagram.com
  12. 12.
    Kim, G., Xing, E.P., Torralba, A.: Modeling and Analysis of Dynamic Behaviors of Web Image Collections. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 85–98. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Kuthirummal, S., Agarwala, A., Goldman, D.B., Nayar, S.K.: Priors for Large Photo Collections and What They Reveal about Cameras. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 74–87. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  14. 14.
    Messier, P.: Notes on dating photographic paper. Topics in Photographic Preservation 11, 123–130 (2005)Google Scholar
  15. 15.
    Nelder, J., Mead, R.: A simplex method for function minimization. Computer Journal (7), 308–313 (1965)Google Scholar
  16. 16.
    Nevercenter Ltd. Co.: Camerabag, http://nevercenter.com/camerabag/mobile/
  17. 17.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision 42(3), 145–175 (2001)MATHCrossRefGoogle Scholar
  18. 18.
    Oliva, A., Torralba, A.: Building the gist of a scene: the role of global image features in recognition. Progress in Brain Research 155 (2006)Google Scholar
  19. 19.
    Schindler, G., Dellaert, F.: Probabilistic temporal inference on reconstructed 3D scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2010)Google Scholar
  20. 20.
    Schindler, G., Dellaert, F., Kang, S.B.: Inferring temporal order of images from 3D structure. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2007)Google Scholar
  21. 21.
    Severa, J.: Dressed for the Photographer: Ordinary Americans & Fashion, 1840-1900. The Kent State University Press (1997)Google Scholar
  22. 22.
    Shrimpton, J.: Family Photographs & How to Date Them. Countryside Books (2008)Google Scholar
  23. 23.
    Storey, N.: Military Photographs & How to Date Them. Countryside Books (2009)Google Scholar
  24. 24.
  25. 25.
    Synthetic, LLC: Hipstamatic, http://hipstamatic.com/
  26. 26.
    Taylor, M.A.: Uncovering Your Ancestry through Family Photographs, 2nd edn. Family Tree Books (2005)Google Scholar
  27. 27.
    Torralba, A., Fergus, R., Freeman, W.T.: 80 million tiny images: a large dataset for non-parametric object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(11), 1958–1970 (2008)CrossRefGoogle Scholar
  28. 28.
    Wilhelm, H., Brower, C.: The Permanence and Care of Color Photographs: Traditional and Digital Color Prints, Color Negatives, Slides, and Motion Pictures. Preservation Publishing Company (1993)Google Scholar
  29. 29.
    Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: Large-scale scene recognition from abbey to zoo. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2010)Google Scholar
  30. 30.
    Yahoo!, Inc.: Flickr: The commons, http://www.flickr.com/commons/

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Frank Palermo
    • 1
  • James Hays
    • 2
  • Alexei A. Efros
    • 1
  1. 1.Carnegie Mellon UniversityPittsburghUSA
  2. 2.Brown UniversityProvidenceUSA

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