Abstract
Multi-level thresholding is a method that is widely used in image segmentation. However, most of the existing methods are not suited to be directly used in applicable fields, and moreover they are not extended into a step of image segmentation. This paper proposes region-based multi-level thresholding as an image segmentation method. At first, we classify pixels of each color channel to two clusters by using EWFCM algorithm that is an improved FCM algorithm with spatial information between pixels. To obtain better segmentation results, a reduction of clusters is then performed by a region-based reclassification step based on a similarity between regions existing in a cluster and the other clusters. We finally perform a region merging by Bayesian algorithm based on Kullback-Leibler distance between a region and the neighboring regions as a post-processing method, as many regions still exist in image. Experiments show that region-based multi-level thresholding is superior to cluster-, pixel-based multi-level thresholding, and an existing method and much better segmentation results are obtained by the proposed post-processing method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging 13(1), 146–165 (2004)
Brink, A.D.: Minimum spatial entropy threshold selection. IEE Proc. Vis. Image Signal Process. 142(3), 128–132 (1995)
Pal, N., Bezdek, J.: On cluster validity for the fuzzy c-means model. IEEE Trans. Fuzzy Syst. 3(3), 370–379 (1995)
Pham, D.L.: Fuzzy clustering with spatial constraints. In: Proc. of IEEE Conf. on Image Process., vol. 2, pp. 65–68 (2002)
Yang, Y., Zheng, C., Lin, P.: Image thresholding based on spatially weighted fuzzy c-means clustering. In: Proc. of IEEE Conf. on Computer and Information Technology, pp. 184–189 (2004)
Du, Y., Chang, C., Thouin, P.D.: Unsupervised approach to color video thresholding. Opt. Eng. 32(2), 282–289 (2004)
Du, Y., Change, C.I., Thouin, P.D.: An unsupervised approach to color video thresholding. In: Proc. of IEEE Conf. on Acoustics, Speech and Signal Processing, vol. 3, pp. 373–376 (2003)
Otsu, N.: A threshold selection method from gray level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray level picture thresholding using the entropy of the histogram. Graph. Models Image Process. 29, 273–285 (1985)
Lloyd, D.E.: Automatic target classification using moment invariant of image shapes. Technical Report, RAE IDN AW 126, Farnborough, UK (1985)
Borsotti, M., Campadelli, P., Schettini, R.: Quantitative evaluation of color image segmentation results. Patt. Recogn. Lett. 19(8), 741–747 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Oh, JT., Kim, WH. (2006). EWFCM Algorithm and Region-Based Multi-level Thresholding. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_107
Download citation
DOI: https://doi.org/10.1007/11881599_107
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45916-3
Online ISBN: 978-3-540-45917-0
eBook Packages: Computer ScienceComputer Science (R0)