Skip to main content
SpringerLink
Log in

Nonparametric Method of Estimating Number of Classes in Image Segmentation

  • COMPUTATIONAL AND DATA ACQUISITION SYSTEMS
  • Published:
Optoelectronics, Instrumentation and Data Processing Aims and scope

Abstract

One of the important problems of automatic threshold image segmentation by brightness are questions of the number of brightness classes and, as a consequence, the required number of thresholds. The solution to the problem of estimating the number of classes in an image is often based on representing its distribution as a mixture of distributions of brightness classes. It is known that this problem (splitting a mixture) has a solution only for some types of distributions, and is difficult to apply when the distributions of brightness classes are unknown. This paper presents a nonparametric method for determining the number of classes based on rank histograms and using the property of the local spatial grouping of elements of each brightness class in the image. Comparison of the proposed method to different criteria for assessing the number of classes in images showed it to be effective.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

REFERENCES

  1. P. A. Chochia, ‘‘Image segmentation based on the analysis of distances in an attribute space,’’ Optoelectron., Instrum. Data Process. 50, 613–624 (2014). https://doi.org/10.3103/S8756699014060107

    Article  Google Scholar 

  2. M. Sezgin and B. Sankur, ‘‘Survey over image thresholding techniques and quantitative performance evaluation,’’ J. Electron. Imaging 13, 146–168 (2004). https://doi.org/10.1117/1.1631315

    Article  ADS  Google Scholar 

  3. J. Kittler and J. Illingworth, ‘‘Minimum error thresholding,’’ Pattern Recognit. 19, 41–47 (1986). https://doi.org/10.1016/0031-3203(86)90030-0

    Article  Google Scholar 

  4. P. A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach (Prentice Hall, Englewood Cliffs, NJ, 1982).

    MATH  Google Scholar 

  5. S. A. Aivazyan, V. M. Bukhshtaber, I. S. Enyukov, and L. D. Meshalkin, Applied Statistics: Classification and Reduction of Dimension (Finansy i Statistika, Moscow, 1989).

    Google Scholar 

  6. H. Akaike, ‘‘A new look at the statistical model identification,’’ IEEE Trans. Autom. Control 19, 716–723 (1974). https://doi.org/10.1109/TAC.1974.1100705

    Article  ADS  MathSciNet  MATH  Google Scholar 

  7. G. Schwarz, ‘‘Estimating the dimension of a model,’’ Ann. Stat. 6, 461–464 (1978).

    Article  MathSciNet  Google Scholar 

  8. S. S. Wilks, Mathematical Statistics (Wiley, New York, 1962).

    MATH  Google Scholar 

  9. P. F. Andrukovich, The Application of the Major Component Method in Practical Studies (Moscow Univ., Moscow, 1973).

    Google Scholar 

  10. J. O. Kim, Factor, Discriminant, and Cluster Analysis (Finansy i Statistika, Moscow, 1989).

    Google Scholar 

  11. A. T. James, ‘‘The distribution of the latent roots of the covariance matrix,’’ Ann. Math. Stat. 31, 151–158 (1960). https://doi.org/10.1214/aoms/1177705994

    Article  MathSciNet  MATH  Google Scholar 

  12. W. Widrow and S. D. Stearns, Adaptive Signal Processing (Prentice-Hall, Englewood Cliffs, NJ, 1985).

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Scientific Research Institute of Electronic Devices, 630081, Novosibirsk, Russia

    R. V. Podrezov

  2. Novosibirsk State Technical University, 630073, Novosibirsk, Russia

    M. A. Raifeld

Authors
  1. R. V. Podrezov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. A. Raifeld
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to R. V. Podrezov or M. A. Raifeld.

Additional information

Translated by L. Trubitsyna

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Podrezov, R.V., Raifeld, M.A. Nonparametric Method of Estimating Number of Classes in Image Segmentation. Optoelectron.Instrument.Proc. 56, 280–287 (2020). https://doi.org/10.3103/S8756699020030139

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S8756699020030139

Keywords:

Search

Navigation