Color-Based Pseudo Object Model for Image Retrieval with Relevance Feedback

  • Tat-Seng Chua
  • Chun-Xin Chu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1554)


Color has been widely used in content-based image retrieval system. The problem with using color is that its representation is low level and hence its retrieval effectiveness is limited. This paper examines the issues related to improving the effectiveness of color-based image retrieval system. It explores the choice of suitable color space and color resolutions for representation and retrieval. This work also emphasizes the use of color coherent vector (CCV) as the basic model for retrieval. CCV is an extension of Color Histogram method to provide low-level representations of objects within the images. A relevance feedback (RF) technique is developed that uses the pseudo object information from relevant images to enhance subsequent retrieval performance. The overall system is tested on a large image database containing over 12,000 images. Tests were performed to evaluate the effectiveness of pseudo object based retrieval method with RF at a number of color resolutions. Results indicate that the RF method is effective and a medium color resolution of 316 colors performs the best.


Color Space Image Retrieval Query Image Relevance Feedback Color Histogram 
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.


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  1. [1]
    D. H. Ballard and C. M. Brown. Computer Vision. Prentice-Hall, Inc, Englewood Cliffs, NJ, 1982.Google Scholar
  2. [2]
    F. Ennesser Bigün and G. Medioni. N-finding waldo and or focus of attention using local color information. IEEE Trans. Pattern Anal., 17(8), 1995.Google Scholar
  3. [3]
    T. S. Chua, Swee kiew Lim, and H. K. Pung. Content-based retrieval of segmented images. In The second ACM International Multimedia Conference, pages 211–218, 1994.Google Scholar
  4. [4]
    Robert S. Gray. Content-based image retrieval: Color and edges. Technical report, Dartmouth University Department of Computer Science technical report 95-252, 1995.Google Scholar
  5. [5]
    Roy Hall. Illumination and Color in Computer Generated Imagery. Springer-Verlag, New York, 1989.Google Scholar
  6. [6]
    Christopher G. Healey and James T. Enns. A perceptual color segmentation algorithm. Technical report, Department of Computer Science, University of British Columbia, 1996.Google Scholar
  7. [7]
    Jing Huang, S Ravi Kumar, and Mandar Mitra. Combing supervised learning with color correlograms for content-based image retrieval. In The Fifth ACM International Multimedia Conference, pages 325–334, 1997.Google Scholar
  8. [8]
    W. Niblack, R. Barber, and W. Equitz. The qbic project: Querying images by content using color, texture, and shape. Technical report, IBM RJ 9203 (81511), February 1993.Google Scholar
  9. [9]
    V. Ogle and M. Stonebraker. Chabot: Retrieval from a relational database of images. IEEE Computer, 28(9):40–48, 1995.Google Scholar
  10. [10]
    Greg Pass and Ramin Zabih. Histogram refinement for content-based image retrieval. In IEEE Workshop on Application of Computer Vision, pages 96–102, 1996.Google Scholar
  11. [11]
    Greg Pass, Ramin Zabih, and Justin Miller. Comparing images using color coherence vectors. In The Fourth ACM International Multimedia Conference, pages 65–73, 1996.Google Scholar
  12. [12]
    A. Pentland, R. Picard, and S. Sclaroff. Photobook: Content-based manipulation of image databases. Intl. Journal of Computer Vision, 18(3):233–254, 1996.CrossRefGoogle Scholar
  13. [13]
    R. Price, T.S Chua, and S Al-Hawamdeh. Applying relevance feedback on a photo archival system. Journal of Information Science, 18:203–215, 1992.CrossRefGoogle Scholar
  14. [14]
    Rosanne J. Price. Applying relevance feedback to a photo archival system. Technical report, Department of Information Systems and Computer Science, National University of Singapore, 1991.Google Scholar
  15. [15]
    Y. Rui, T.S. Huang, and S. Mehrotra. Content-based image retrieval with relevance feedback in mars. In Proceedings of IEEE International Conference on Image, 1997.Google Scholar
  16. [16]
    Gerard Salton. Automatic Text Processing. Addison-Wesley Publishing Company, Cornell University, 1989.Google Scholar
  17. [17]
    Gerard Salton and Buckley. Introduction to Modern Information Retrieval. McGraw-Hill Inc., 1983.Google Scholar
  18. [18]
    M.J. Shen. Image retrieval using multiple attributes with relevance feedback. Technical report, Dept of Information Systems and Computer Science, National University of Singapore, 1996.Google Scholar
  19. [19]
    J. R. Smith and S.-F. Chang. Tools and techniques for color image retrieval. Symposium on Electronic Imaging: Science and Technology: Storage and Retrieval for Image and Video Databases IV, 2670:426–437, 1996.Google Scholar
  20. [20]
    John R. Smith. Integrated Spatial and Feature Image Systems: Retrieval, Analysis and Compression. PhD thesis, Columbia University, 1997.Google Scholar
  21. [21]
    Michael J. Swain and Dana H. Ballard. Color indexing. International Journal of Computer Vision, 7(1):11–32, 1991.CrossRefGoogle Scholar
  22. [22]
    Xia Wan and C. C. Jay Kuo. Color distribution analysis and quantization for image retrieval. In SPIE:Storage and Retrieval for Still Image and Video Databases’96(IV), 1996.Google Scholar
  23. [23]
    Jia Wang, Wen jann Yang, and Raj Acharya. Color clustering techniques for color-content-based image retrieval from image databases. Proceedings of the International Conference on Multimedia Computing and Systems, pages 442–449, 1997.Google Scholar
  24. [24]
    Hsu Wynne, T. S. Chua, and H. K. Pung. Integrated color-spatial approach to content-based image retrieval. In The third ACM International Multimedia Conference, pages 305–313, 1995.Google Scholar
  25. [25]
    G. Wyszecki and W. S. Stiles. Color science: concepts and methods, quantitative data and formulae. The Wiley series in pure and applied optics. John Wiley and Sons, Inc., New York, 1982.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Tat-Seng Chua
    • 1
  • Chun-Xin Chu
    • 1
  1. 1.School of ComputingNational University of SingaporeSingapore

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