Toward a Personalized CBIR System

  • Chih-Yi Chiu 
  • Hsin-Chih Lin 
  • Shi-Nine Yang 
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2314)


A personalized CBIR system based on a unified framework of fuzzy logic is proposed in this study. The user preference in image retrieval can be captured and stored in a personal profile. Thus, images that appeal to the user can be effectively retrieved. Our system provides users with textual descriptions, visual examples, and relevance feedbacks in a query. The query can be expressed as a query description language, which is characterized by the proposed syntactic rules and semantic rules. In our system, the semantic gap problem can be eliminated by the use of linguistic terms, which are represented as fuzzy membership functions. The syntactic rules refer to the way that linguistic terms are generated, whereas the semantic rules refer to the way that the membership function of each linguistic term is generated. The problem of human perception subjectivity can be eliminated by the proposed profile updating and feature re-weighting methods. Experimental results have proven the effectiveness of our system.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aigrain, P., Zhang, H. J., Petkovic, D.: Content-Based Representation and Retrieval of Visual Media: A State-of-The-Art Review. Multimedia Tools and Applications 3 (1996) 179–202CrossRefGoogle Scholar
  2. 2.
    Idris, F., Panchanathan, S.: Review of Image and Video Indexing Techniques. Journal of Visual Communication and Image Representation 8 (1997) 146–166CrossRefGoogle Scholar
  3. 3.
    Rui, Y., Huang, T. S., Chang, S. F.: Image Retrieval: Current Techniques, Promising Directions, and Open Issues. Journal of Visual Communication and Image Representation 10 (1999) 39–62CrossRefGoogle Scholar
  4. 4.
    Smeulders, A. W. M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-Based Image Retrieval at the End of the Early Years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (2000) 1349–1380CrossRefGoogle Scholar
  5. 5.
    Minka, T. P., Picard, R. W.: Interactive Learning with a Society of Models. Pattern Recognition 30 (1997) 565–582CrossRefGoogle Scholar
  6. 6.
    Rui, Y., Huang, T. S., Mehrotra, S.: Content-Based Image Retrieval with Relevance Feedback in MARS. IEEE International Conference on Image Processing, Vol. 2, Santa Barbara, CA, USA (1997) 815–818Google Scholar
  7. 7.
    Lu, Y., Hu, C., Zhu, X., Zhang, H. J., Yang, Q.: A Unified Framework for Semantics and Feature Based Relevance Feedback in Image Retrieval Systems. ACM International Conference on Multimedia, Los Angeles, CA, USA (2000) 31–37Google Scholar
  8. 8.
    Tamura, H., Mori, S., Yamawaki, T.: Texture Features Corresponding to Visual Perception. IEEE Transactions on Systems, Man, and Cybernetics 8 (1978) 460–473CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Chih-Yi Chiu 
    • 1
  • Hsin-Chih Lin 
    • 2
  • Shi-Nine Yang 
    • 1
  1. 1.Department of Computer ScienceNational Tsing Hua UniversityHsinchuTaiwan
  2. 2.Department of Information ManagementChang Jung Christian UniversityTainanTaiwan

Personalised recommendations