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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsRecommended Reading
Adams R. and Bischof L. Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell., 16(6):641–647, 1994.
Belloulata K. and Konrad J. Fractal image compression with region-based functionality. IEEE Trans. Image Process., 11(4):351–362, 2002.
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.
Donoho D., Johnstone I., Kerkyacharian G., and Picard D. Density estimation by wavelet thresholding. Ann. Statist., 24:508–539, 1996.
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.
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.
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.
Ji L. and Yan H. Attractable snakes based on the greedy algorithm for contour extraction. Pattern Recognit., 35(4):791–806, 2002.
Kass M., Witkin A., and Terzopoulos D. Snakes: active contour models. Int. J. Comput. Vis., 1(4):321–331, 1987.
Mehnert A. and Jackway P. An improved seeded region growing algorithm. Pattern Recognit. Lett., 18(10):1065–1071, 1997.
Otsu N. A threshold selection method from gray-level histogram. IEEE Trans. Syst., Man, Cybern., 9(1):62–66, 1979.
Pachowicz P.W. Semi-autonomous evolution of object models for adaptive object recognition. IEEE Trans. Syst. Man Cybern., 24(8):1191–1207, 1994.
Pal N.R. and Pal S.K. A review on image segmentation techniques. Pattern Recognit., 26(9):1277–1294, 1993.
Pavlidis T. and Liow Y.T. Integrating region growing and edge detection. IEEE Trans. Pattern Anal. Mach. Intell., 12(3):225–233, 1990.
Shih F.Y. and Cheng S. Automatic seeded region growing for color image segmentation. Image Vis. Comput., 23(10):877–886, 2005.
Shih F.Y. and Zhang K. Efficient contour detection based on improved snake model. Pattern Recognit. Artif. Intell., 18(2):197–209, 2004.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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
DOI: https://doi.org/10.1007/978-0-387-39940-9_1011
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-35544-3
Online ISBN: 978-0-387-39940-9
eBook Packages: Computer ScienceReference Module Computer Science and Engineering