Skip to main content

Image Segmentation

  • Reference work entry

Synonyms

Region segmentation; Pixel classification; Edge detection; Thresholding

Definition

The rapid rate of image analysis field has grown enormously in the past few decades. Image analysis intends to construct explicit, meaningful descriptions of physical objects in images. It can be divided into two parts: low-level image analysis and high-level image analysis. Low-level tasks focus on region-based segmentation, whereas high-level tasks are related to object-oriented representation. Image segmentation, a process of pixel classification, aims to extract or segment objects or regions from the background. Intrinsic images can be generated at the low-level processing, revealing physical properties of the imaged scene. This can often be implemented with parallel computation.

Historical Background

Image segmentation is a critical step to the success of object recognition [12], image compression [2], image visualization [7], and image retrieval [3]. Pal and Pal [13] provided a review on...

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   2,500.00
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

Learn about institutional subscriptions

Recommended Reading

  1. Adams R. and Bischof L. Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell., 16(6):641–647, 1994.

    Article  Google Scholar 

  2. Belloulata K. and Konrad J. Fractal image compression with region-based functionality. IEEE Trans. Image Process., 11(4):351–362, 2002.

    Article  Google Scholar 

  3. Chen Y. and Wang J.Z. A region-based fuzzy feature matching approach to content-based image retrieval. IEEE Trans. Pattern Anal. Mach. Intell., 24(9):1252–1267, 2002.

    Article  Google Scholar 

  4. Donoho D., Johnstone I., Kerkyacharian G., and Picard D. Density estimation by wavelet thresholding. Ann. Statist., 24:508–539, 1996.

    Article  MathSciNet  MATH  Google Scholar 

  5. Fan J., Yau D.K., Elmagarmid A.K., and Aref W.G. Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Trans. Image Process., 10(10):1454–1466, 2001.

    Article  MATH  Google Scholar 

  6. Haris K., Efstratiadis S.N., Maglaveras N., and Katsaggelos A.K. Hybrid image segmentation using watersheds and fast region merging. IEEE Trans. Image Process., 7(12):1684–1699, 1998.

    Article  Google Scholar 

  7. Hartmann S.L. and Galloway R.L. Depth-buffer targeting for spatially accurate 3-D visualization of medical images. IEEE Trans. Med. Imaging 19(10):1024–1031, 2000.

    Article  Google Scholar 

  8. Ji L. and Yan H. Attractable snakes based on the greedy algorithm for contour extraction. Pattern Recognit., 35(4):791–806, 2002.

    Article  MATH  Google Scholar 

  9. Kass M., Witkin A., and Terzopoulos D. Snakes: active contour models. Int. J. Comput. Vis., 1(4):321–331, 1987.

    Article  Google Scholar 

  10. Mehnert A. and Jackway P. An improved seeded region growing algorithm. Pattern Recognit. Lett., 18(10):1065–1071, 1997.

    Article  Google Scholar 

  11. Otsu N. A threshold selection method from gray-level histogram. IEEE Trans. Syst., Man, Cybern., 9(1):62–66, 1979.

    Article  MathSciNet  Google Scholar 

  12. Pachowicz P.W. Semi-autonomous evolution of object models for adaptive object recognition. IEEE Trans. Syst. Man Cybern., 24(8):1191–1207, 1994.

    Article  Google Scholar 

  13. Pal N.R. and Pal S.K. A review on image segmentation techniques. Pattern Recognit., 26(9):1277–1294, 1993.

    Article  Google Scholar 

  14. Pavlidis T. and Liow Y.T. Integrating region growing and edge detection. IEEE Trans. Pattern Anal. Mach. Intell., 12(3):225–233, 1990.

    Article  Google Scholar 

  15. Shih F.Y. and Cheng S. Automatic seeded region growing for color image segmentation. Image Vis. Comput., 23(10):877–886, 2005.

    Article  Google Scholar 

  16. Shih F.Y. and Zhang K. Efficient contour detection based on improved snake model. Pattern Recognit. Artif. Intell., 18(2):197–209, 2004.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this entry

Cite this entry

Shih, F.Y. (2009). Image Segmentation. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_1011

Download citation

Publish with us

Policies and ethics