An Intelligent Clustering Method for Highly Similar Digital Photos Using Pyramid Matching with Human Perceptual 25 Color Histogram

  • Dong-Sung Ryu
  • Kwanghwi Kim
  • Hwan-Gue Cho
Part of the Communications in Computer and Information Science book series (CCIS, volume 200)


Recently, as the number of photos to be managed grows, photo classification becomes one of the most burdensome tasks. Besides, these technical advances encourage people to take duplicate photos for the more clear and the more user-wanted photos. This paper presents an automated clustering method to classify hundreds of photos considering the people’s recent photographing behavior. First, we partition the input photo sets into trivial event groups. Then, we employ an interval graph considering their color similarity from temporally consecutive photos to construct each similar photo group. For this clustering, we used 25 color block histogram based on pyramid matching. The user experiment shows that our algorithm is enough correct to classify hundreds of photos.


photo clustering pyramid matching maximal clique finding 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cooper, M., Foote, J., Girgensohn, A., Wilcox, L.: Temporal event clustering for digital photo collections. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP), 269–288 (2005)Google Scholar
  2. 2.
    Dong-Sung, R., Kwang Hwi, K., Sun-Young, P., Hwan-Gue, C.: A web-based photo management system for large photo collections with user-customizable quality assessment. In: Proc. of the ACM Symposium on Applied Computing, pp. 1229–1236 (2011)Google Scholar
  3. 3.
    Platt, J.C.: AutoAlbum: clustering digital photographs using probabilistic model merging. In: Proc. of IEEE Workshop on Content-based Access of Image and Video Libraries, pp. 96–100 (2000)Google Scholar
  4. 4.
    Graham, A., Garcia-Molina, H., Paepcke, A., Winograd, T.: Time as essence for photo browsing through personal digital libraries. In: Proc. of the 2nd ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 326–335 (2002)Google Scholar
  5. 5.
    Platt, J.C., Czerwinski, M., Field, B.A.: PhotoTOC: automatic clustering for browsing personal photographs. In: Proc. of the IEEE Joint Conference of the 4th Pacific Rim Conference on Multimedia, pp. 6–10 (2003)Google Scholar
  6. 6.
    Toyama, K., Logan, R., Roseway, A.: Geographic location tags on digital images. In: Proc. of the 11th ACM International Conference on Multimedia, pp. 156–166 (2003)Google Scholar
  7. 7.
    Boutell, M., Luo, J.: A generalized temporal context model for semantic scene classification. Multimedia Systems, 82–92 (2005)Google Scholar
  8. 8.
    Yang, H., Wang, Q.: Grouping and summarizing scene images from web collections. In: Proc. of the 5th International Symposium on Advances in Visual Computing, pp. 315–324 (2009)Google Scholar
  9. 9.
    Chuljin, J., TaeJin, Y., Hwan-Gue, C.: A smart clustering algorithm for photo set obtained from multiple digital cameras. In: Proc. of the ACM Symposium on Applied Computing, pp. 1784–1791 (2009)Google Scholar
  10. 10.
    Grauman, K., Trevor, D.: The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features. In: Proc. of ICCV, pp. 1458–1465 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dong-Sung Ryu
    • 1
  • Kwanghwi Kim
    • 1
  • Hwan-Gue Cho
    • 1
  1. 1.Dept. of Computer SciencePusan National UniversityBusanKorea

Personalised recommendations