The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.03 - July-Sept. (2013 vol.20)
pp: 58-70
Zanoni Dias , University of Campinas, Brazil
Siome Goldenstein , University of Campinas, Brazil
Anderson Rocha , University of Campinas, Brazil
ABSTRACT
Similar to organisms that evolve in biology, a document can change slightly overtime, and each new version may, in turn, generate other versions. Multimedia phylogeny investigates the history and evolutionary process of digital objects and includes finding the causal and ancestral document relationships, source of modifications, and the order and transformations that originally created the set of near duplicates. Multimedia phylogeny has direct applications in security, forensics, and information retrieval. This article explores the phylogeny problem for near-duplicate images in large-scale scenarios and present solutions that have straightforward extension to other media such as videos. Experiments with approximately 2 million test cases (with synthetic and real data) show that the proposed methods automatically build image phylogeny trees from partial information about the near duplicates, improving the efficiency and effectiveness of the whole process, and represent a step forward in determining causal relationships between digital images overtime.
INDEX TERMS
Multimedia communication, Image processing, Search methods, Digital systems, Object recognition, Image matching, near-duplicate recognition, near-duplicate search, multimedia, multimedia applications, multimedia phylogeny, image phylogeny, image dependencies, ancestral relationships, near-duplicate detection
CITATION
Zanoni Dias, Siome Goldenstein, Anderson Rocha, "Large-Scale Image Phylogeny: Tracing Image Ancestral Relationships", IEEE MultiMedia, vol.20, no. 3, pp. 58-70, July-Sept. 2013, doi:10.1109/MMUL.2013.17
REFERENCES
1. S. Lohr, "The Age of Big Data," New York Times,12 Feb. 2012.
2. A. Weigend, "The Social Data Revolution(s)," Harvard Business Rev.,20 May 2009.
3. "Big Data, Big Impact: New Possibilities for International Development," tech report, World Economic Forum, 2012.
4. C. Xiao et al., "Efficient Similarity Joins for Near-Duplicate Detection," ACM Trans. Database Systems, vol. 36, no. 3, 2011, article no. 15.
5. H. Shen et al., "Near-Duplicate Video Retrieval: Current Research and Future Trends," IEEE Multimedia, 2013, to appear.
6. Y. Maret, "Efficient Duplicate Detection Based on Image Analysis," doctoral thesis, École Polytechnique Fédérale de Lausanne, Switzerland, 2007.
7. A. Joly, O. Buisson, and C. Frélicot, "Content-Based Copy Retrieval Using Distortion-Based Probabilistic Similarity Search," IEEE Trans. Multimedia, vol. 9, no. 2, 2007, pp. 293–306.
8. Z. Dias, A. Rocha, and S. Goldenstein, "Image Phylogeny by Minimal Spanning Trees," IEEE Trans. Image Forensics and Security, vol. 7, no. 2, 2012, pp. 774–788.
9. L. Kennedy and S.-F. Chang, "Internet Image Archaeology: Automatically Tracing the Manipulation History of Photographs on the Web," Proc. ACM Conf. Multimedia (MM), ACM, 2008, pp. 349–358.
10. Z. Dias, A. Rocha, and S. Goldenstein, "First Steps Toward Image Phylogeny," Proc. IEEE Workshop Information Forensics and Security, IEEE, 2010, pp. 1–6.
11. A.D. Rosa et al., "Exploring Image Dependencies: a New Challenge in Image Forensics," Proc. Media Forensics and Security II, SPIE, 2010, pp. X1–X12.
12. Z. Dias, A. Rocha, and S. Goldenstein, "Video Phylogeny: Recovering Near-Duplicate Video Relationships," Proc. IEEE Workshop Information Forensics and Security, IEEE, 2011, pp. 1–6.
13. H. Bay, T. Tuytelaars, and L. V. Gool, "SURF: Speeded Up Robust Features," Proc. European Conf. Computer Vision, Springer-Verlag, 2006, pp. 1–14.
14. M. Fischler and R. Bolles, "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography," Comm. ACM, vol. 24, no. 6, 1981, pp. 381–395.
15. G. Schaefer and M. Stich, "UCID: An Uncompressed Colour Image Database," Proc. SPIE Storage and Retrieval Methods and Applications for Multimedia, SPIE, 2004.
16. A. Rocha et al., "Vision of the Unseen: Current Trends and Challenges in Digital Image and Video Forensics," ACM Computing Surveys, vol. 43, no. 4, 2011, article no. 26.
582 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool