Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
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)
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)
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)
Boutell, M., Luo, J.: A generalized temporal context model for semantic scene classification. Multimedia Systems, 82–92 (2005)
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)
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)
Grauman, K., Trevor, D.: The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features. In: Proc. of ICCV, pp. 1458–1465 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ryu, DS., Kim, K., Cho, HG. (2011). An Intelligent Clustering Method for Highly Similar Digital Photos Using Pyramid Matching with Human Perceptual 25 Color Histogram. In: Kim, Th., Adeli, H., Robles, R.J., Balitanas, M. (eds) Information Security and Assurance. ISA 2011. Communications in Computer and Information Science, vol 200. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23141-4_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-23141-4_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23140-7
Online ISBN: 978-3-642-23141-4
eBook Packages: Computer ScienceComputer Science (R0)