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Concurrent photo sequence organization

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Abstract

Personal photo album organization is a highly demanding domain where advanced tools are required to manage large photo collections. In contrast to many previous works, that try to solve the problem of organizing a single user photo sequence, we present a new technique to account for the concurrent photo sequence organization problem, that is the problem of organizing multiple photo sequences taken during the same event. Given a set of sequences acquired at the same place during the same temporal window by several users using different cameras, our framework is intended to capture the evolution of the event and groups photos based on temporal proximity and visual content. The method automatically organizes the reference sequence in a tree capturing the event structure. Such a structure is then used to align the remaining photo sequences to the reference one. We tested our approach on the publicly available Gallagher dataset and on a new dataset we collected; this new dataset is composed of four photo sequences taken by four users at a public event. Results demonstrate the effectiveness of our method.

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Notes

  1. The code may be found at http://www.featurespace.org/.

References

  1. Ardizzone E, La Cascia M, Vella F (2008) Mean shift clustering for personal photo album organization. In: International Conference on Image Processing, (ICIP). IEEE, San Diego, CA, pp 85–88, 12–15 Oct 2008

    Google Scholar 

  2. Bay H, Tuytelaars T, Van Gool L (2006) Surf: speeded up robust features. In: Proc of European Conf on Computer Vision (ECCV). Springer, Graz, Austria, pp 404–417, 7–13 May 2006

    Google Scholar 

  3. Bishop C (2006) Pattern recognition and machine learning, vol 4. Springer, New York

    Google Scholar 

  4. Choi J, Yang S, Ro Y, Plataniotis K (2008) Face annotation for personal photos using context-assisted face recognition. In: Proc of int conf on Multimedia Information Retrieval (MIR). ACM, Vancouver, Canada, pp 44–51, 30–31 Oct 2008

    Google Scholar 

  5. Chu WT, Lee YL, Yu JY (2009) Using context information and local feature points in face clustering for consumer photos. In: Proc of Int Conf on Acoustics, Speech, and Signal Processing (ICASSP). IEEE, Taipei, Taiwan, pp 1141–1144, 19–24 Apr 2009

    Google Scholar 

  6. Cooper M, Foote J, Girgensohn A, Wilcox L (2005) Temporal event clustering for digital photo collections. ACM Trans Multi Commun App (TOMCCAP) 1(3):269–288

    Article  Google Scholar 

  7. Facebook (2004) http://www.facebook.com

  8. Fei-Fei L, Perona P (2005) A Bayesian hierarchical model for learning natural scene categories. In: Proc of int conf on Computer Vision and Pattern Recognition (CVPR), vol 2. IEEE, San Diego, CA, pp 524–531, 20–26 June 2005

    Google Scholar 

  9. Gallagher A, Chen T (2008) Clothing cosegmentation for recognizing people. In: Proc of Computer Vision and Pattern Recognition (CVPR). IEEE, Anchorage, Alaska, 23–28 June 2008

    Google Scholar 

  10. Gong B, Jain R (2008) Hierarchical photo stream segmentation using context. In: Proceedings of IS&T/SPIE, vol 6820. SPIE, San Francisco, CA, p 682003

    Google Scholar 

  11. Google+ (2011) http://plus.google.com/

  12. iPhoto (2009) http://www.apple.com/ilife/iphoto

  13. Jaimes A, Benitez A, Chang S, Loui A (2002) Discovering recurrent visual semantics in consumer photographs. In: Proc of Int Conf on Image Processing (ICIP), vol 3. IEEE, Rochester, New York, pp 528–531, 22–25 Sept 2002

    Google Scholar 

  14. Jang C, Yoon T, Cho H (2010) Digital photo classification methodology for groups of photographers. Multimed Tools Appl 50(3):441–463

    Article  Google Scholar 

  15. Jiang H, Yu S (2009) Linear solution to scale and rotation invariant object matching. In: Proc of int conf on Computer Vision and Pattern Recognition (CVPR). IEEE, Miami, FL, pp 2474–2481, 20–25 June 2009

    Google Scholar 

  16. Leow W, Li R (2004) The analysis and applications of adaptive-binning color histograms. Comput Vis Image Underst (CVIU) 94(1–3):67–91

    Article  Google Scholar 

  17. Li C, Chiu C, Huang C, Chen C, Chien L (2006) Image content clustering and summarization for photo collections. In: Proc of Int Conf on Multimedia and Expo (ICME). IEEE, Toronto, Canada, pp 1033–1036, 9–12 July 2006

    Google Scholar 

  18. Li SZ (2005) Markov random field—modeling in computer vision. Springer-Verlag

  19. Lin D, Kapoor A, Hua G, Baker S (2010) Joint people, event, and location recognition in personal photo collections using cross-domain context. In: Proc of Eur Conf on Computer Vision (ECCV). Springer, Crete, Greece, pp 243–256, 5–11 Sept 2010

    Google Scholar 

  20. Lo Presti L, Morana M, La Cascia M (2010) A data association algorithm for people re-identification in photo sequences. In: Int Symposium on Multimedia (ISM). IEEE, Taichung, Taiwan, pp 318–323, 13–15 Dec 2010

    Google Scholar 

  21. Lo Presti L, Morana M, La Cascia M (2011) A data association approach to detect and organize people in personal photo collections. Multimed Tools Appl 1–32. doi:10.1007/s11042-011-0839-5

    Google Scholar 

  22. Lowe D (1999) Object recognition from local scale-invariant features. In: Proc of Int Conference on Computer Vision (ICCV), vol 2. IEEE, Kerkyra, Greece, pp 1150–1157, 20–27 Sept 1999

    Chapter  Google Scholar 

  23. Matas J, Chum O, Urban M, Pajdla T (2004) Robust wide-baseline stereo from maximally stable extremal regions. Image Vis Comput 22(10):761–767

    Article  Google Scholar 

  24. Mikolajczyk K, Tuytelaars T, Schmid C, Zisserman A, Matas J, Schaffalitzky F, Kadir T, Gool L (2005) A comparison of affine region detectors. Int J Comput Vis 65(1):43–72

    Article  Google Scholar 

  25. Sandhaus P, Boll S (2011) Semantic analysis and retrieval in personal and social photo collections. Multimed Tools Appl 51(1):5–33

    Article  Google Scholar 

  26. Tavanapong W, Zhou J (2004) Shot clustering techniques for story browsing. Trans Multimed 6(4):517–527

    Article  Google Scholar 

  27. Wu K, Yang M (2007) Mean shift-based clustering. Pattern Recognition 40:3035–3052

    Article  MATH  Google Scholar 

  28. Zhang L, Chen L, Li M, Zhang H (2003) Automated annotation of human faces in family albums. In: Proc of conf on multimedia (MM). ACM, Berkeley, CA, pp 355–358, 2–8 Nov 2003

    Google Scholar 

  29. Zhang L, Hu Y, Li M, Ma W, Zhang H (2004) Efficient propagation for face annotation in family albums. In: Proc of conf on multimedia (MM). ACM, New York, NY, pp 716–723, 10–16 Oct 2004

    Google Scholar 

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Acknowledgements

We thank all the anonymous reviewers and the associate editor whose insightful comments and very constructive reviews led to significant improvements of the manuscript.

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Correspondence to Liliana Lo Presti.

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This research has been conducted while Dr. Lo Presti was post-doctoral researcher at University of Palermo.

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Lo Presti, L., La Cascia, M. Concurrent photo sequence organization. Multimed Tools Appl 68, 777–803 (2014). https://doi.org/10.1007/s11042-012-1079-z

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