Skip to main content
SpringerLink
Log in

A clustering method for concurrent photos obtained from multiple cameras using max-flow network model

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

With the popularization of digital cameras, the use of several cameras by group photographers at the same event is becoming common. Photographers can share their contents and even take pictures of each other. So it is becoming important to manage concurrent photos from multiple cameras in order to classify many accumulated photos into proper clusters. In this paper, we propose a novel photo clustering method based on the max-flow network algorithm, and we visualize a network graph for cluster verification. To apply our algorithm, input concurrent photos are used to create an edge-weighted graph structure. In order to transform the photo clustering problem into a graph partition one, first we need to construct an Augmented Concurrent photo Graph (ACG) and then rewrite our original problem in terms of the graph partition one using the min-cut max-flow network model. The previous methods dealt with photo clustering as a 1-D problem using a linear partition. But we consider clustering for concurrent group photos as a 2-D partition based on other users’ photo contents. Each photo is used to create a node and similarities between photos are used to create the edge weights (capacities) of the network. We partition the network into two subgraphs according to the min-cut, which represents the weakest edge connections between the photos. Using repeated graph partitions for each subgraph (sub-network), we can obtain suitable subgraphs corresponding to photo clusters. The graph construction or partition can be adjusted according to user preferences in order to obtain the intended results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29

Similar content being viewed by others

References

  1. ACDSee: ACDSee Photo Manager Software. http://www.acdsee.com/ (2010)

  2. Algorithmic Solutions Software GmbH: LEDA C++ class library. http://www.algorithmic-solutions.com/leda/index.htm (2010)

  3. Balakrishnan, N.: Handbook of the Logistic Distribution. Dekker, New York (1992)

    MATH  Google Scholar 

  4. Bederson, B.B.: PhotoMesa: a zoomable image browser using quantum treemaps and bubblemaps. In: UIST ’01: Proceedings of the 14th Annual ACM Symposium on User Interface Software and Technology, pp. 71–80 (2001)

  5. Boll, S., Sandhaus, P., Scherp, A., Westermann, U.: Semantics, content, and structure of many for the creation of personal photo albums. In: MULTIMEDIA’07, pp. 641–650 (2007)

  6. Boutell, M., Luo, J.: Bayesian fusion of camera metadata cues in semantic scene classification. In: CVPR’04, vol. 2, pp. 623–630 (2004)

  7. Boutell, M., Luo, J.: Photo classification by integrating image content and camera metadata. In: ICPR’04, vol. 4, pp. 901–904 (2004)

  8. Boutell, M., Luo, J., Brown, C.: A generalized temporal context model for classifying image collections. Multimed. Syst. 11(1), 82–92 (2005)

    Article  Google Scholar 

  9. Boykov, Y., Funka-Lea, G.: Graph cuts and efficient n-d image segmentation. Int. J. Comput. Vision 70(2), 109–131 (2006)

    Article  Google Scholar 

  10. Boykov, Y., Jolly, M.-P.: Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In: Proceedings of International Conference on Computer Vision, pp. 105–112 (2001)

  11. Chen, J.-C., Chu, W.-T., Kuo, J.-H., Weng, C.-Y., Wu, J.-L.: Tiling slideshow. In: MULTIMEDIA’06, pp. 25–34 (2006)

  12. Chu, W.-T., Chen, J.-C., Wu, J.-L.: Tiling slideshow: an audiovisual presentation method for consumer photos. IEEE Multimed. 14(3), 36–45 (2007)

    Article  Google Scholar 

  13. Chu, W.-T., Lin, C.-H.: Automatic selection of representative photo and smart thumbnailing using near-duplicate detection. In: Proceeding of the 16th ACM International Conference on Multimedia, MM’08, pp. 829–832 (2008)

  14. Cooper, M., Foote, J., Girgensohn, A., Wilcox, L.: Temporal event clustering for digital photo collections. ACM Trans. Multimed. Comput. Commun. Appl. 1(3), 269–288 (2005)

    Article  Google Scholar 

  15. Cui, J., Wen, F., Xiao, R., Tian, Y., Tang, X.: Easyalbum: an interactive photo annotation system based on face clustering and re-ranking. In: Proceedings of the SIGCHI conference on human factors in computing systems, CHI’07, pp. 367–376. ACM, New York (2007)

  16. Deselaers, T., Keysers, D., Ney, H.: Features for image retrieval: an experimental comparison. Inform. Retr. 11, 77–107 (2008)

    Article  Google Scholar 

  17. Girgensohn, A., Adcock, J., Cooper, M., Foote, J., Wilcox, L.: Simplifying the management of large photo collections. In: INTERACT03, IOS, pp. 196–203 (2003)

  18. Goldberg, A.V., Rao, S.: Beyond the flow decomposition barrier. J ACM 45(5), 783–797 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  19. Graham, A., Garcia-Molina, H., Paepcke, A., Winograd, T.: Time as essence for photo browsing through personal digital libraries. In: JCDL’02, pp. 326–335 (2002)

  20. Hsieh, C.-C., Cheng, W.-H., Chang, C.-H., Chuang, Y.-Y., Wu, J.-L.: Photo navigator. In: MULTIMEDIA’08, pp. 419–428. ACM, New York (2008)

  21. Jang, C., Yoon, T., Cho, H.-G.: A smart clustering algorithm for photo set obtained from multiple digital cameras. In: ACM SAC ’09, pp. 1184–1191 (2009)

  22. Jang, C.-J., Lee, J.-Y., Lee, J.-W., Cho, H.-G.: Smart management system for digital photographs using temporal and spatial features with exif metadata. In: ICDIM’07, pp. 110–115 (2007)

  23. Japan Electronics and Information Technology Industries Assoc. Exif version 2.2 digital still camera image file format standard (Exif) version 2.2 (2002)

  24. Kwatra, V., Schödl, A., Essa, I., Turk, G., Bobick, A.: Graphcut textures: image and video synthesis using graph cuts. ACM Trans. Graph. 22, 277–286 (2003)

    Article  Google Scholar 

  25. Lee, B.N., Chen, W.-Y., Chang, E.Y.: A scalable service for photo annotation, sharing, and search. In: MULTIMEDIA’06, pp. 699–702 (2006)

  26. Li, Y., Sun, J., Shum, H.-Y.: Video object cut and paste. ACM Trans. Graph. 24, 595–600 (2005)

    Article  Google Scholar 

  27. Lim, J.-H., Tian, Q.: Home photo content modeling for personalized event-based retrieval. IEEE Multimed. 10(4), 28–37 (2003)

    Article  Google Scholar 

  28. Loui, A., Savakis, A.: Automated event clustering and quality screening of consumer pictures for digital albuming. Multimed. IEEE Trans. 5(3), 390–402 (2003)

    Article  Google Scholar 

  29. Masilamani, V., Krithivasan, K.: A min-cost-max-flow based algorithm for reconstructing binary image from two projections using similar images. In: Proceedings of the 12th international conference on Combinatorial image analysis, IWCIA’08, pp. 408–419 (2008)

  30. Mei, T., Wang, B., Hua, X.-S., Zhou, H.-Q., Li, S.: Probabilistic multimodality fusion for event based home photo clustering. In: Proceedings of the IEEE International Conference on Multimedia and Expo, pp. 1757–1760 (2006)

  31. Mills, T.J., Pye, D., Sinclair, D., Wood, K.R.: Shoebox: A digital photo management system. Technical Report 2000.10, AT&T Laboratories Cambridge, Cambridge (2000)

  32. Naaman, M., Song, Y.J., Paepcke, A., Garcia-Molina, H.: Automatic organization for digital photographs with geographic coordinates. In: Proceedings of the 4th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL’04, pp. 53–62 (2004)

  33. Google: Picasa - free photo editing software. http://picasa.google.com/(2010)

  34. Platt, J.C.: Autoalbum: Clustering digital photographs using probabilistic model merging. cbaivl 00, 96 (2000)

  35. Platt, J.C., Czerwinski, M., Field, B.A.: Phototoc: automatic clustering for browsing personal photographs. In: ICICS-PCM 2003, pp. 6–10 (2003)

  36. Quack, T., Leibe, B., Gool, L.V.: World-scale mining of objects and events from community photo collections. In: CIVR’08, pp. 47–56 (2008)

  37. Rother, C., Kolmogorov, V., Blake, A.: "grabcut": interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23, 309–314 (2004)

    Article  Google Scholar 

  38. Ryu, D.-S., Chung, W.-K., Cho, H.-G.: Photoland: a new image layout system using spatio-temporal information in digital photos. In: Proceedings of the 2010 ACM Symposium on Applied Computing, SAC’10, pp. 1884–1891 (2010)

  39. Ryu, D.-S., Kim, S., Cho, H.-G.: A photo visualization system considering region of interest. In: Proceedings of the IEEE International Conference on Computer and Information Technology (CIT), pp. 560–566 (2010)

  40. Sinha, P., Jain, R.: Classification and annotation of digital photos using optical context data. In: CIVR’08, pp. 309–318 (2008)

  41. Wang, C., Jing, F., Zhang, L., Zhang, H.-J.: Scalable search-based image annotation. Multimed. Syst. 14(4), 205–220 (2008)

    Article  Google Scholar 

  42. West, D.B.: Introduction to Graph Theory, 2nd edn. Prentice-Hall, Englewood Cliff (2001)

    Google Scholar 

  43. Yu, H., Bennamoun, M., Chua, C.-S.: An extension of min/max flow framework. Image Vision Comput. 27, 342–353

  44. Zeng, Y., Samaras, D., Chen, W., Peng, Q.: Topology cuts: a novel min-cut/max-flow algorithm for topology preserving segmentation in n-d images. Comput. Vis. Image Underst., 112(1), 81–90 (2008)

    Article  Google Scholar 

  45. Zhang, R., Zhang, Z.M., Li, M., Ma, W.-Y., Zhang, H.-J.: A probabilistic semantic model for image annotation and multi-modal image retrieval. Multimed. Syst. 12(1), 27–33 (2006)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the IT R&D program of MKE/MCST/IITA (2008-F-031-01, Development of Computational Photography Technologies for Image and Video Contents).

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Pusan National University, Pusan, Republic of Korea

    Chuljin Jang & Hwan-Gue Cho

Authors
  1. Chuljin Jang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hwan-Gue Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hwan-Gue Cho.

Additional information

Communicated by Wei-Ying Ma.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jang, C., Cho, HG. A clustering method for concurrent photos obtained from multiple cameras using max-flow network model. Multimedia Systems 18, 295–317 (2012). https://doi.org/10.1007/s00530-011-0250-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-011-0250-0

Keywords

Search

Navigation