Abstract
We describe an approach to image segmentation based on a two-layer module that is executed until a good segmentation is achieved, providing an evolution of previous segmentation results at each execution. The first layer performs a global segmentation of an image of decreasing area at each evolution by adopting a genetic algorithm learning technique to select segmentation parameters that give better results. The second layer provides the input to the next evolution by selecting the segmented regions that need further optimisation. A main goal of our system is to perform the segmentation without using neither ground-truth information nor human judgement. Thus, edge detection is performed to assess the performance of region segmentation and to guide the evolution of segmentation. Experimental results are consistent with what is observed visually.
Chapter PDF
References
S.M.Bhandarkar, Y.Zhang, W.D.Potter, An Edge Detection Technique Using GA-Based Optimisation, Pattern Recognition, 27(9), 1159–1180, (1994).
B.Bhanu, S.Lee, J.Ming, Adaptive Image Segmentation using a genetic algorithm, IEEE Trans. on SMC, 25(12), 1543–1567, (1995).
D.N.Chun, H.S.Yang, Robust image segmentation using genetic algorithm with a fuzzy measure, Pattern Recognition, 29(7), 1195–1211, (1996).
D.E.Goldberg, Genetic Algorithms in Search, Optimisation, and Machine Learning, Addison-Wesley, Reading, MA, (1989).
J.F.Haddon and J.F.Boyce, Image Segmentation by unifying region and boundary information, IEEE Trans. on PAMI, 12(10); 929–948, (1990).
R.M.Haralick, L.G.Shapiro, Image segmentation techniques, Computer Vision, Graphics, and Image Processing, 29, 100–132, (1985).
M.K.Hu, Visual problem recognition by moment invariants, IRE Trans. Inf. Theory, 8, 179–187, (1962).
A. J. Katz, P. R. Thrift, Generating Image Filters for Target Recognition by Genetic Learning, IEEE Trans. on PAMI, 16(9); 906–910, (1994).
N.R.Pal and S.K.Pal, A review on image segmentation techniques, Pattern Recognition, 26, 1277–1294, (1993).
J.T.Pavlidis and Y.T.Liow, Integrating region growing and edge detection, IEEE Trans. on PAMI, 12(3); 225–233, (1990).
G.Tascini, P.Puliti, P.Zingaretti, Region Detection in grey-level images, in “Progress in Image Analysis and Processing“, Cantoni, Cordella, Levialdi, Sanniti di Baja Eds., World Scientific Publ. Co., Singapore, 106–110, (1990).
S.Wang and R.M.Haralick, Automatic multithreshold selection, Computer Vision, Graphics, and Image Processing, 25, 46–67, (1984).
J.S. Weszka and A.Rosenfeld, Histogram modification for threshold selection, IEEE Trans. on SMC, 9(1), 38–52, (1979).
Y.J.Zhang, A survey on evaluation methods for image segmentation, Pattern Recognition, 29(8), 1335–1346, (1996).
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zingaretti, P., Carbonaro, A., Puliti, P. (1997). Evolutionary image segmentation. In: Del Bimbo, A. (eds) Image Analysis and Processing. ICIAP 1997. Lecture Notes in Computer Science, vol 1310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63507-6_208
Download citation
DOI: https://doi.org/10.1007/3-540-63507-6_208
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63507-9
Online ISBN: 978-3-540-69585-1
eBook Packages: Springer Book Archive