Web Image Retrieval Refinement by Visual Contents

  • Zhiguo Gong
  • Qian Liu
  • Jingbai Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4016)


For Web image retrieval, two basic methods can be used for representing and indexing Web images. One is based on the associate text around the Web images; and the other utilizes visual features of images, such as color, texture, shape, as the descriptions of Web images. However, those two methods are often applied independently in practice. In fact, both have their limitations to support Web image retrieval. This paper proposes a novel model called ’multiplied refinement’, which is more applicable to combination of those two basic methods. Our experiments compare three integration models, including multiplied refinement model, linear refinement model and expansion model, and show that the proposed model yields very good performance.


Image Retrieval Prototype System Average Precision Image Query 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.
    Chua, T.S., et al.: A Concept-based Image Retrieval System. In: Proceedings of 27th Annual Hawaii International Conference on System Science, Maui, Hawaii, January 4-7, pp. 590–598 (1994)Google Scholar
  2. 2.
    Gong, Z., Leong Hou, U., Cheang, C.W.: An Implementation of Web Image Search Engines. In: Chen, Z., Chen, H., Miao, Q., Fu, Y., Fox, E., Lim, E.-p. (eds.) ICADL 2004. LNCS, vol. 3334, pp. 355–367. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Ashley, J., et al.: The Query By Image Content (QBIC) System. In: SIGMOD Conference, p. 475 (1995)Google Scholar
  4. 4.
    Saykol, E., Güdükbay, U., Ulusoy, Ö.: Integrated Querying of Images by Color, Shape, and Texture Content of Salient Objects. In: ADVIS, pp. 363–371 (2004)Google Scholar
  5. 5.
    Smith, J.R., Chang, S.-F.: Single Color Extraction and Image Query. In: ICIP 1995 (1995)Google Scholar
  6. 6.
    Smith, J.R., Chang, S.-F.: Automated Image Retrieval Using Color and Texture. In: Pattern Analysis and Machine Intelligence, PAMI (1996)Google Scholar
  7. 7.
    Smith, J.R., Chang, S.-F.: Tools and Techniques for Color Image Retrieval. In: Storage and Retrieval for Image and Video Databases (SPIE), pp. 426–437 (1996)Google Scholar
  8. 8.
    Smith, J.R., Chang, S.-F.: TVisualSEEk: A Fully Automated Content-Based Image Query System. ACM Multimedia, 87–98 (1996)Google Scholar
  9. 9.
    Zhuang, Y., Li, Q., Lau, R.W.H.: Web-Based Image Retrieval: A Hybrid Approach. Computer Graphics International, 62–72 (2001)Google Scholar
  10. 10.
    Lu, G., Williams, B.: An Integrated WWW Image Retrieval System (1999),
  11. 11.
    Chang, C.C., Lee, S.Y.: Retrieval of similar pictures on pictorial databases. Pattern Recogn. 24, 675–681 (1991)CrossRefGoogle Scholar
  12. 12.
    Harmandas, V., Sanderson, M., Dunlop, M.D.: Image Retrieval by Hypertext Links. In: SIGIR, pp. 296–303 (1997)Google Scholar
  13. 13.
    Shen, H.T., Ooi, B.C., Tan, K.-L.: Giving meanings to WWW images. In: MULTIMEDIA 2000: Proceedings of the eighth ACM international conference on Multimedia, pp. 39–47 (2000)Google Scholar
  14. 14.
    Kato, T.: Database Architecture for Content-Based Image Retrieval. In: Proceedings of Society of the Photo-Optical Instrumentation Engineers: Image Storage and Retrieval, 1662, San Jose, California, USA. SPIE (1992)Google Scholar
  15. 15.
    Yanai, K.: Generic image classification using visual knowledge on the web. ACM Multimedia, 167–176 (2003)Google Scholar
  16. 16.
    Aslandogan, Y.A., Yu, C.T.: Multiple evidence combination in image retrieval: diogenes searches for people on the Web. In: SIGIR, pp. 88–95 (2000)Google Scholar
  17. 17.
    Chen, Z., et al.: Web mining for Web image retrieval. JASIST 52, 831–839 (2001)CrossRefGoogle Scholar
  18. 18.
    Puzicha, J., et al.: Empirical Evaluation of Dissimilarity Measures for Color and Texture. In: ICCV, pp. 1165–1172 (1999)Google Scholar
  19. 19.
  20. 20.
    Mandal, M.K., Aboulnasr, T.: Fast wavelet histogram techniques for image indexing. Comput. Vis. Image Underst. 75, 1077–3142 (1999)CrossRefGoogle Scholar
  21. 21.
  22. 22.
    Pass, G., Zabih, R., Miller, J.: Comparing Images Using Color. ACM Multimedia, 65–73 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhiguo Gong
    • 1
  • Qian Liu
    • 1
  • Jingbai Zhang
    • 1
  1. 1.Faculty of Science and TechnologyUniversity of MacauMacaoPRC

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