Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

Active learning in very large databases

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Query-by-example and query-by-keyword both suffer from the problem of “aliasing,” meaning that example-images and keywords potentially have variable interpretations or multiple semantics. For discerning which semantic is appropriate for a given query, we have established that combining active learning with kernel methods is a very effective approach. In this work, we first examine active-learning strategies, and then focus on addressing the challenges of two scalability issues: scalability in concept complexity and in dataset size. We present remedies, explain limitations, and discuss future directions that research might take.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Blum A, Mitchell T (1998) Combining labeled and unlabeled data wih co-training. In: Proceedings of the workshop on computational learning theory, Madison, Wisconsin, 92–100

  2. Brinker K (2003) Incorporating diversity in active learning with support vector machines. In: Prooceedings of the twentieth international conference on machine learning, Washington, District of Columbia, 59–66

  3. Chang E, Goh K, Sychay G, Wu G (2003a) CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines. IEEE Trans Circuits Syst Video Technol 13(1):26–38 (Special issue on conceptual and dynamic aspects of multimedia content description)

    Article  Google Scholar 

  4. Chang E, Li B (2003) MEGA—the maximizing expected generalization algorithm for learning complex query concepts. ACM Trans Inf. Sys. 21(4):347–382

    Article  MathSciNet  Google Scholar 

  5. Chang E, Li B, Wu G, Goh K-S (2003b) Statistical learning for effective visual information retrieval. In: IEEE Conference in Image Processing, Barcelona, Spain, 606–612

  6. Flickner M, Sawhney H, Ashley J, Huang Q, Dom B, Gorkani M, Hafner J, Lee D, Petkovic D, Steele D, Yanker P (1995) Query by image and video content: the QBIC system. IEEE Computer 28(9):23–32

    Google Scholar 

  7. Goh K, Chang EY, Lai W-C (2004) Concept-dependent multimodal active learning for image retrieval. In: ACM international conference on multimedia, New York, New York, 564–571

  8. Li B, Chang, E (2003) Discovery of a perceptual distance function for measuring image similarity. ACM Multimedia J. 8(6):512–522 (Special issue on content-based image retrieval)

    Article  Google Scholar 

  9. Li C, Chang E, Garcia-Molina H, Wiederhold G (2002) Clustering for approximate similarity queries in high-dimensional spaces. IEEE Trans Knowl Data Eng. 14(4):792–808

    Article  Google Scholar 

  10. Panda N, Chang E (2005) Exploiting geometry for support vector machine indexing. In: SIAM conference on data mining, Newport Beach, California

  11. Tong S, Chang E (2001) Support vector machine active learning for image retrieval. In: Proceedings of ACM international conference on multimedia, Ottawa, Canada, 107–118

  12. Tong S, Koller D (2000) Support vector machine active learning with applications to text classification. In: Proceedings of the 17th international conference on machine learning, Stanford, USA, 401–412

  13. Vapnik V (1995) The nature of statistical learning theory. Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

  14. Zhang Z, Wu G, Wang G, Chang E (2005) Bayesian kernel regression. In: International conference on machine learning, Bonn, Germany

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Navneet Panda.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Panda, N., Goh, KS. & Chang, E.Y. Active learning in very large databases. Multimed Tools Appl 31, 249–267 (2006). https://doi.org/10.1007/s11042-006-0043-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-006-0043-1

Keywords

Navigation