Advertisement

Spatial Discriminant Function with Minimum Error Rate for Image Segmentation

  • EunSang Bak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3211)

Abstract

This paper describes how a normal discriminant function with minimum error rate can be applied to segment an image in a particular manner. Since the maximum likelihood method assigns pixels based on the underlying distributions in image, it is inevitable to make decision errors when there are overlapping areas between the underlying distributions. However, this overlapping area can be minimized by a conversion of distributions which is proposed in this paper. This method is derived by exploiting characteristics of a linear combination of random variables and its relation to the corresponding random vector. The suitable performance of the process is mathematically proved and the experimental results that support the effectiveness of the proposed method are provided.

Keywords

Image Segmentation Random Vector Discriminant Function Neighboring Pixel Decision Error 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chou, W.: Discriminant-Function-Based Minimum Recognition Error Rate Pattern-Recognition Approach to Speech Recognition. Proc. IEEE 88, 1201–1223 (2000)CrossRefGoogle Scholar
  2. 2.
    Hastie, T., Tibshirani, R.: Discriminant Adaptive Nearest Neighbor Classification. IEEE Trans. Pattern Anal. Machine Intell. 18, 607–616 (1996)CrossRefGoogle Scholar
  3. 3.
    Kurita, T., Otsu, N., Abdelmalek, N.: Maximum Likelihood Thresholding Based on Population Mixture Models. Pattern Recognition 25, 1231–1240 (1992)CrossRefGoogle Scholar
  4. 4.
    Mardia, K.V., Hainsworth, T.J.: A Spatial Thresholding Method for Image Segmentation. IEEE Trans. Pattern Anal. Machine Intell. 10, 919–927 (1988)CrossRefGoogle Scholar
  5. 5.
    Sakai, M., Yoneda, M., Hase, H.: A New Robust Quadratic Discriminant Function. In: Proc. Int’l. Conf. Pattern Recognition (ICPR1998), pp. 99–102 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • EunSang Bak
    • 1
  1. 1.Electrical and Computer Engineering DepartmentUniversity of North Carolina at CharlotteCharlotteU.S.A

Personalised recommendations