Large scale image copy detection evaluation

ABSTRACT

In this paper we provide a comparative study of content-based copy detection methods, which include research literature methods based on salient point matching (SURF), discrete cosine and wavelet transforms, color histograms, biologically motivated visual matching and other methods. In our evaluation we focus on large-scale applications, especially on performance in the context of search engines for web images. We assess the scalability of the tested methods by investigating the detection accuracy relative to descriptor size, description time per image and matching time per image. For testing, original images altered by a diverse set of realistic transformations are embedded in a collection of one million web images.

REFERENCES

• 1: Julien Law-To , Li Chen , Alexis Joly , Ivan Laptev , Olivier Buisson , Valerie Gouet-Brunet , Nozha Boujemaa , Fred Stentiford, Video copy detection: a comparative study, Proceedings of the 6th ACM international conference on Image and video retrieval, p.371-378, July 09-11, 2007, Amsterdam, The Netherlands [doi>10.1145/1282280.1282336]
• 2: Narayanan Shivakumar , Hector Garcia-Molina, Building a scalable and accurate copy detection mechanism, Proceedings of the first ACM international conference on Digital libraries, p.160-168, March 20-23, 1996, Bethesda, Maryland, United States [doi>10.1145/226931.226961]
• 3: Herbert Bay , Andreas Ess , Tinne Tuytelaars , Luc Van Gool, Speeded-Up Robust Features (SURF), Computer Vision and Image Understanding, v.110 n.3, p.346-359, June, 2008 [doi>10.1016/j.cviu.2007.09.014]
• 4: Chang, E., Wang, J., Li, C., and Wiederhold, G. 1998. RIME - a replicated image detector for the world--wide web. In Proceedings of SPIE Symposium of Voice, Video, and Data Communications, 68--77.
• 5: Kim, C. 2003. Content-based image copy detection. Signal Processing: Image Communication 18, 3, 169--184.
• 6: Cox, I. J., Kilian, J., Leighton, F. T., and Shamoon, T. 1997. Secure spread spectrum watermarking for multimedia. IEEE Transactions on Image Processing 6, 12, 1673--1687.
• 7: Nicu Sebe , Michael S. Lew, Color-based retrieval, Pattern Recognition Letters, v.22 n.2, p.223-230, Feb. 2001 [doi>10.1016/S0167-8655(00)00092-1]
• 8: Krystian Mikolajczyk , Cordelia Schmid, A Performance Evaluation of Local Descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.27 n.10, p.1615-1630, October 2005 [doi>10.1109/TPAMI.2005.188]
• 9: Cano, P., Batlle, T, Kalker, T, and Haitsma, J. 2002. A review of algorithms for audio fingerprinting. In Proceedings of International Workshop on Multimedia Signal Processing, 169--173.
• 10: Christian Böhm , Stefan Berchtold , Daniel A. Keim, Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases, ACM Computing Surveys (CSUR), v.33 n.3, p.322-373, September 2001 [doi>10.1145/502807.502809]
• 11: Jun Jie Foo , Justin Zobel , Ranjan Sinha , S. M. M. Tahaghoghi, Detection of near-duplicate images for web search, Proceedings of the 6th ACM international conference on Image and video retrieval, p.557-564, July 09-11, 2007, Amsterdam, The Netherlands [doi>10.1145/1282280.1282360]
• 12: Jun Jie Foo , Ranjan Sinha, Pruning SIFT for scalable near-duplicate image matching, Proceedings of the eighteenth conference on Australasian database, p.63-71, January 30-February 02, 2007, Ballarat, Victoria, Australia
• 13: Yan Ke , Rahul Sukthankar , Larry Huston, An efficient parts-based near-duplicate and sub-image retrieval system, Proceedings of the 12th annual ACM international conference on Multimedia, October 10-16, 2004, New York, NY, USA [doi>10.1145/1027527.1027729]
• 14: Chao-Yong Hsu , Chun-Shien Lu, Geometric distortion-resilient image hashing system and its application scalability, Proceedings of the 2004 workshop on Multimedia and security, September 20-21, 2004, Magdeburg, Germany [doi>10.1145/1022431.1022448]
• 15: Foo, J. J., Sinha, R., and Zobel, J. 2007. Discovery of image versions in large collections. Lecture Notes in Computer Science 4352, Springer-Verlag, 433--442.
• 16: Levine, M. D. 1985. Vision in man and machine, McGraw-Hill, 574.
• 17: Independent JPEG Group, http://www.ijg.org
• 18: David G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, v.60 n.2, p.91-110, November 2004 [doi>10.1023/B:VISI.0000029664.99615.94]
• 19: Michael S. Lew , Nicu Sebe , Chabane Djeraba , Ramesh Jain, Content-based multimedia information retrieval: State of the art and challenges, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP), v.2 n.1, p.1-19, February 2006 [doi>10.1145/1126004.1126005]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Clustering

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Indexing methods
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.9 Applications

General Terms:
Algorithms, Experimentation, Measurement, Performance

Keywords:
content-based image copy detection, image redundancy, web search