Advertisement

A Method for Evaluating the Performance of Content-Based Image Retrieval Systems Based on Subjectively Determined Similarity between Images

  • John A. BlackJr.
  • Gamal Fahmy
  • Sethuraman Panchanathan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2383)

Abstract

In recent years multimedia researchers have attempted to design content-based image retrieval systems. However, despite the development of these systems, the term “content” has still remained rather ill defined, and this has made the evaluation of such systems problematic. This paper proposes a method for the creation of a reference image set in which the similarity of each image pair is estimated by two independent methods — by the subjective evaluation of human observers, and by the use of “visual content words” as basis vectors that allow the multidimensional content of each image to be represented with a content vector. The similarity measure computed with these content vectors is shown to correlate with the subjective judgment of human observers, and thus provides both a more objective method for evaluating and expressing image content, and a possible path to automating the process of content-based indexing in the future.

Keywords

Reference Image Image Retrieval Human Visual System Query Image Content Base Image Retrieval 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chang, E. Y., Li Beitao, and Li Chen. “Toward Perception-Based Image Retrieval.” Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries. IEEE Comput. Soc Los Alamitos CA USA, 2000. viii+119.Google Scholar
  2. 2.
    Frese, T., C. A. Bouman, and J. P. Allebach. “Methodology for Designing Image Similarity Metrics Based on Human Visual System Models.” Proceedings of the SPIE The Intl Society for Optical Engineering 3016 (1997): 472–83.Google Scholar
  3. 3.
    Jorgensen, C, and R. Srihari. “Creating a Web-Based Image Database for Benchmarking Image Retrieval Systems.” Proceedings of the SPIE The International Society for Optical Engineering 3644 (1999): 534–41.Google Scholar
  4. 4.
    Kam, A. H., et al. “Content Based Image Retrieval through Object Extraction and Querying.“ Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries. IEEE Comput. Soc Los Alamitos CA USA, 2000. viii+119.Google Scholar
  5. 5.
    La Cascia, M., S. Sethi, and S. Sclaroff. “Combining Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web.” Proceedings. IEEE Workshop on Content-Based Access of Image and Video Libraries (Cat. No.98EX173). IEEE Comput. Soc Los Alamitos CA USA, 1998. viii+115.Google Scholar
  6. 6.
    Leung, T., and J. Malik. “Recognizing Surfaces Using Three-Dimensional Textons.” Proceedings of the Seventh IEEE International Conference on Computer Vision. IEEE Comput. Soc Los Alamitos CA USA, 1999. 2 vol. xxvii+1258.Google Scholar
  7. 7.
    Ma, W. Y., Deng Yining, and B. S. Manjunath. “Tools for Texture/Color Based Search of Images.” Proceedings of the SPIE The International Society for Optical Engineering 3016 (1997): 496–507.Google Scholar
  8. 8.
    Manmatha, R., and S. S. Ravela. “Syntactic Characterization of Appearance and Its Application to Image Retrieval.“ Proceedings of the SPIE The International Society for Optical Engineering 3016 (1997): 484–95.Google Scholar
  9. 9.
    MacArthur, S. D., C. E. Brodley, and Shyu Chi Ren. “Relevance Feedback Decision Trees in Content-Based Image Retrieval.” Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries. IEEE Comput. Soc Los Alamitos CA USA, 2000. viii+119.Google Scholar
  10. 10.
    Ravishankar Rao, A. “Identifying High Level Features of Texture Perception.” CVGIP: Graphical Models and Image Processing 55.3 (1993): 218–33.Google Scholar
  11. 11.
    Shyu, C. R., et al. “Local Versus Global Features for Content-Based Image Retrieval.” Proceedings. IEEE Workshop on Content-Based Access of Image and Video Libraries (Cat. No.98EX173). IEEE Comput. Soc Los Alamitos CA USA, 1998. viii+115.Google Scholar
  12. 12.
    Smith, J. R. “Image Retrieval Evaluation.” Proceedings. IEEE Workshop on Content-Based Access of Image and Video Libraries (Cat. No.98EX173). IEEE Comput. Soc Los Alamitos CA USA, 1998. viii+115.Google Scholar
  13. 13.
    Yihong, Gong, G. Proietti, and C. Faloutsos. “Image Indexing and Retrieval Based on Human Perceptual Color Clustering.” Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231). IEEE Comput. Soc Los Alamitos CA USA, 1998. xvii+970.Google Scholar
  14. 14.
    The NaturePix reference image set is in the public domain, and may be downloaded at http://cubic.asu.edu/vccl/imagesets/naturepix.

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • John A. BlackJr.
    • 1
  • Gamal Fahmy
    • 1
  • Sethuraman Panchanathan
    • 1
  1. 1.Visual Computing and Communications LabArizona State UniversityTempeUSA

Personalised recommendations