Skip to main content

Feedback

               Feedback.                                                           
Volume 6, Issue 2pp 131–155Cite as

Relevance Feedback and Learning in Content-Based Image Search

  • Hongjiang Zhang
  • Zheng Chen
  • Mingjing Li
  • Zhong Su
Article

Abstract

A major bottleneck in content-based image retrieval (CBIR) systems or search engines is the large gap between low-level image features used to index images and high-level semantic contents of images. One solution to this bottleneck is to apply relevance feedback to refine the query or similarity measures in image search process. In this paper, we first address the key issues involved in relevance feedback of CBIR systems and present a brief overview of a set of commonly used relevance feedback algorithms. Almost all of the previously proposed methods fall well into such framework. We present a framework of relevance feedback and semantic learning in CBIR. In this framework, low-level features and keyword annotations are integrated in image retrieval and in feedback processes to improve the retrieval performance. We have also extended framework to a content-based web image search engine in which hosting web pages are used to collect relevant annotations for images and users' feedback logs are used to refine annotations. A prototype system has developed to evaluate our proposed schemes, and our experimental results indicated that our approach outperforms traditional CBIR system and relevance feedback approaches.
image retrieval relevance feedback machine learning web mining 

Preview

Unable to display preview. Download preview PDF.

References

  1. [1]
    C. Buckley and G. Salton, “Optimization of relevance feedback weights,” in Proceedings of SIGIR'95, 1995.Google Scholar
  2. [2]
    S. K. Chang, C. W. Yan, D. C. Dimitroff, and T. Arndt, “An intelligent image database system,” IEEE Transactions on Software Engineering 14(5), 1988.Google Scholar
  3. [3]
    Z. Chen, W. Liu, C. Hu, M. Li, and H. J. Zhang, “iFind: A web image search engine,” in Proceedings of SIGIR2001, 2001.Google Scholar
  4. [4]
    Z. Chen, W. Liu, F. Zhang, M. Li, and H. J. Zhang, “Web mining for web image retrieval,” Journal of the American Society for Information Science and Technology52(10), August 2001, 831-839.Google Scholar
  5. [5]
    I. J. Cox, T. P. Minka, T. V. Papathomas, and P. N. Yianilos, “The Bayesian image retrieval system, PicHunter: Theory, implementation, and psychophysical experiments,” IEEE Transactions on Image Processing, Special Issue on Digital Libraries, 2000.Google Scholar
  6. [6]
    M. Flickner, H. Sawhney, W. Niblack et al., “Query by image and video content: The QBIC system,” IEEE Computer Magazine28, 1995, 23-32.Google Scholar
  7. [7]
    J. Huang, S. R. Kumar, and M. Metra, “Combining supervised learning with color correlograms for contentbased image retrieval,” in Proceedings of ACM Multimedia'95, November 1997, pp. 325-334.Google Scholar
  8. [8]
    Y. Ishikawa, R. Subramanya, and C. Faloutsos, “Mindreader: Query databases through multiple examples,” in Proceedings of the 24th VLDB Conference, New York, 1998.Google Scholar
  9. [9]
    F. Jing, M. Li, H. J. Zhang, and B. Zhang, “An effective region-based image retrieval framework,” in Proceedings of ACM Multimedia 2002, Juan-les-Pins, France, December 1-6, 2002.Google Scholar
  10. [10]
    J. Laaksonen, M. Koskela, and E. Oja, “PicSOM: Self-organizing maps for content-based image retrieval,” in Proceedings of International Joint Conference on NN, July 1999.Google Scholar
  11. [11]
    C. Lee, W. Y. Ma, and H. J. Zhang, “Information embedding based on user's relevance feedback for image retrieval,” in Proceedings of SPIE International Conference on Multimedia Storage and Archiving Systems IV, Boston, 19-22 September 1999.Google Scholar
  12. [12]
    Y. Lu et al., “A unified framework for semantics and feature based relevance feedback in image retrieval systems,” in Proceedings of ACM MM2000, 2000.Google Scholar
  13. [13]
    S. D. MacArthur, C. E. Brodley, and C.-R. Shyu, “Relevance feedback decision trees in content-based image retrieval,” in IEEE Workshop on Content-Based Access of Image and Video Libraries, 2000, pp. 68-72.Google Scholar
  14. [14]
    T. Minka and R. Picard, “Interactive learning using a 'Society of Models',” Pattern Recognition30(4), 1997.Google Scholar
  15. [15]
    T. Mitchell, Machine Learning, McGraw-Hill, 1997.Google Scholar
  16. [16]
    J. J. Rocchio Jr., “Relevance feedback in information retrieval,” in The SMART Retrieval System: Experiments in Automatic Document Processing, ed. G. Salton, Prentice-Hall, 1971, pp. 313-323.Google Scholar
  17. [17]
    Y. Rui and T. S. Huang, “A novel relevance feedback technique in image retrieval,” in Proceedings of 7th ACM Conference on Multimedia, 1999.Google Scholar
  18. [18]
    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 Processing, 1997.Google Scholar
  19. [19]
    G. Salton, Automatic Text Processing, Addison-Wesley, Reading, MA, 1989.Google Scholar
  20. [20]
    G. Salton and M. McGill, Introduction to Modern Information Retrieval, McGraw-Hill, 1983.Google Scholar
  21. [21]
    S. Sclaroff, L. Taycher, and M. L. Cascia, “ImageRover: a content-based image browser for theWorldWide Web,” Technical Report 97-005, Boston University CS Dept., 1997.Google Scholar
  22. [22]
    H. T. Shen, B. C. Ooi, and K. L. Tan, “Giving meanings to WWW images,” in Proceedings of ACM MM2000, 2000, pp. 39-48.Google Scholar
  23. [23]
    Z. Su, S. Li, and H. J. Zhang, “Extraction of feature subspaces for content-based retrieval using relevance feedback,” in ACM Multimedia 2001, Ottawa, Canada, 2001.Google Scholar
  24. [24]
    Z. Su, H. J. Zhang, and S. Ma, “Relevant feedback using a Bayesian classifier in content-based image retrieval,” in SPIE Electronic Imaging 2001, San Jose, CA, January 2001.Google Scholar
  25. [25]
    K. Tieu and P. Viola, “Boosting image retrieval,” in IEEE Conference on Computer Vision and Pattern Recognition, 2000.Google Scholar
  26. [26]
    S. Tong and E. Chang, “Support vector machine active leaning for image retrieval,” in ACM Multimedia 2001, Ottawa, Canada, 2001.Google Scholar
  27. [27]
    N. Vasconcelos and A. Lippman, “Learning from user feedback in image retrieval systems,” in NIPS'99, Denver, CO, 1999.Google Scholar
  28. [28]
    P. Wu and B. S. Manjunath, “Adaptive nearest neighbour search for relevance feedback in large image database,” in ACM Multimedia Conference, Ottawa, Canada, 2001.Google Scholar
  29. [29]
    Y. Wu, Q. Tian, and T. S. Huang, “Discriminant EM algorithm with application to image retrieval,” in IEEE CVPR, South Carolina, 2000.Google Scholar
  30. [30]
    H. J. Zhang and D. Zhong, “A scheme for visual feature based image indexing,” in Proceedings of IS&T/SPIE Conference on Storage and Retrieval for Image and Video Databases III, 1995, pp. 36-46.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Hongjiang Zhang
    • 1
  • Zheng Chen
    • 1
  • Mingjing Li
    • 1
  • Zhong Su
    • 1
  1. 1.Microsoft Research AsiaBeijingChina

Comments

Popular posts from this blog

Top 10 software company in the world 2019

Top 10  software company  in the world  2019.                             GeniusJarvis.blogspot.com .                                           Top 10 Largest IT and Software Companies in the World (2019) May 13, 2019   Ahmed Top Listed 0 Comments         3.7 (20) As technology endure to improve the future of how we develop business, it has become significant to develop relationships with worldwide tech brands. Just think once- Will be able to get information about  Top 10 Information Technology Companies in the world  today without access of the internet? Possibly your answer is ‘NO’ or ‘Not so easy as internet’. We’ve done the hard work and number creaking to provide you with the widest list of largest software c...

Birth certificate Wikipedia in up

 Birth certificate Wikipedia in up.                                                             celindiya.blogspot.com .                                                Open main menu Search National Register of Citizens of India Read in another language Watch this page Edit The logo used by NRC, Assam The  National Register of Citizens (NRC) (ৰাষ্ট্ৰীয় নাগৰিক পঞ্জীকৰণ) is a register containing names of all genuine  Indian citizens. The register was first prepared after the 1951 Census of India.  [1] [2]   Census of India . [3] The NRC is now being updated in Assam to include the names of those persons (or their descendants) who appear in the NRC, 1951...

Get sllabus from this top 5 University

Get sllabus for learn hacking to here top 5.                                                                                           vpnMentor vpnMentor     Blog     The Top 5 Places to Learn Ethical Hacking Online in 2019 Blog Views: 12,899,621 Posts: 1,311 Follow our experts 12932 8343 Best VPNs by Category   Best VPNs Overall   Best VPNs for Mac   Best VPNs for iOS   Best VPNs for Torrents   Best VPNs for Windows   Best VPNs for Android   Best VPNs for USA  VPN Blog Posts   How to Watch Disney+ Online Anywhere in 2019   How to Use WhatsApp in China – Complete Guide 2019   5 Best FREE VPNs for Mac...