Skip to main content

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1983))

  • 1337 Accesses

Abstract

This paper presents an incremental algorithm for classification problems using hierarchical discriminant analysis for real-time learning and testing applications. Virtual labels are automatically formed by clustering in the output space. These virtual labels are used for the process of deriving discriminating features in the input space. This procedure is performed recursively in a coarse-to-fine fashion resulting in a tree, called incremental hierarchical discriminating regression (IHDR) method. Embedded in the tree is a hierarchical probability distribution model used to prune unlikely cases. A sample size dependent negativelog- likelihood (NLL) metric is used to deal with large-sample size cases, small-sample size cases, and unbalanced-sample size cases, measured among different internal nodes of the IHDR algorithm. We report the experimental results of the proposed algorithm for an OCR classification problem and an image orientation classification problem.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. H. Murase and S. K. Nayar, “Visual learning and recognition of 3-D objects from appearance,” Int’l Journal of Computer Vision, vol. 14, no. 1, pp. 5–24, January 1995.

    Google Scholar 

  2. D. L. Swets and J. Weng, “Using discriminant eigenfeatures for image retrieval,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 831–836, 1996.

    Article  Google Scholar 

  3. W.-S. Hwang, J. Weng, M. Fang, and J. Qian, “A fast image retrieval algorithm with automatically extracted discriminant features,” in Proc. IEEE Workshop on Content-based Access of Image and Video Libraries, Fort Collins, Colorado, June 1999, pp. 8–15.

    Google Scholar 

  4. Juyang Weng and Wey-Shiuan Hwang, “An incremental method for building decision trees for high dimensional input space,” Tech. Rep. MSU-CSE-00-4, Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, March 2000.

    Google Scholar 

  5. Y. Le Cun, L. D. Jackel, L. Bottou, A. Brunot, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, U. A. Muller, E. Sackinger, P. Simard, and V. Vapnik, “Comparison of learning algorithms for handwritten digit recognition,” in International Conference on Arti_cial Neural Networks, F. Fogelman and P. Gallinari, Eds., Paris, 1995, pp. 53–60, EC2 & Cie.

    Google Scholar 

  6. A. Vailaya, H.J. Zhang, and A. Jain, “Automatic image orientation detection,” in IEEE International Conference on Image Processing, Kobe, Japan, October 25–28, 1999.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hwang, WS., Weng, J. (2000). Hierarchical Discriminant Regression for Incremental and Real-Time Image Classification. In: Leung, K.S., Chan, LW., Meng, H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44491-2_71

Download citation

  • DOI: https://doi.org/10.1007/3-540-44491-2_71

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41450-6

  • Online ISBN: 978-3-540-44491-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics