Abstract
A graph-based approach for image segmentation that employs genetic algorithms is proposed. An image is modeled as a weighted undirected graph, where nodes correspond to pixels, and edges connect similar pixels. A fitness function, that extends the normalized cut criterion, is employed, and a new concept of nearest neighbor, that takes into account not only the spatial location of a pixel, but also the affinity with the other pixels contained in the neighborhood, is defined. Because of the locus-based representation of individuals, the method is able to partition images without the need to set the number of segments beforehand. As experimental results show, our approach is able to segment images in a number of regions that well adhere to the human visual perception.
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 subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Chen, C.W., Luo, J., Parker, K.J.: Image segmentation via adaptive k-means clustering and knowledge-based morphological operations with biomedical applications. IEEE Transactions on Image Processing 7(12), 1673–1683 (1998)
Chen, S., Zhang, K.: Robust image segmentation using fcm with spatial constrains based on a new kernel-induced distance measure. IEEE Transactions on Systems Man and Cybernetics B 34, 1907–1916 (2004)
Cour, T., Bénézit, F., Shi, J.: Spectral segmentation with multiscale graph decomposition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005)), pp. 1124–1131 (2005)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. John Wiley & Sons, New York (1973)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. International Journal of Computer Vision 59(2), 167–181 (2004)
Di Gesú, V., Lo Bosco, G.: Image Segmentation Based on Genetic Algorithms Combination. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 352–359. Springer, Heidelberg (2005)
Halder, A., Pathak, N.: An evolutionary dynamic clustering based colour image segmentation. International Journal of Image Processing 4, 549–556 (2011)
Helterbrand, J.D.: One pixel-wide closed boundary identification. IEEE Transactions on Image Processing 5(5), 780–783 (1996)
Jiao, L.: Evolutionary-based image segmentation methods. Image Segmentation (10), 180–224 (2011)
Lai, C.-C., Chang, C.-Y.: A hierarchical evolutionary algorithm for automatic medical image segmentation. Expert Syst. Appl. 36(1), 248–259 (2009)
Leung, T., Malik, J.: Contour Continuity in Region Based Image Segmentation. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 544–559. Springer, Heidelberg (1998)
Merzougui, M., Allaoui, A.E., Nasri, M., Hitmy, M.E., Ouariachi, H.: Evolutionary image segmentation by pixel classification and the evolutionary Xie and Beni criterion - application to quality control. International Journal of Computational Intelligence and Information Security 2(8), 4–13 (2011)
Pappas, T.N.: An adaptive clustering algorithms for image segmentation. IEEE Transactions on Signal Processing 40(4), 901–914 (1992)
Park, Y.J., Song, M.S.: A genetic algorithm for clustering problems. In: Proc. of 3rd Annual Conference on Genetic Algorithms, pp. 2–9 (1989)
Paulinas, M., Uinskas, A.: A survey of genetic algorithms applications for image enhancement and segmentation. Information Technology And Control, Kaunas, Technologija 36(3), 278–284 (2007)
Sahoo, P.K., Soltani, S., Wong, A.K.C.: A survey of thresholding techniques. CVGIP 41, 233–260 (1988)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)
Urquhart, R.: Graph theoretical clustering based on limited neighborhood sets. Pattern Recognition 15(3), 173–187 (1982)
Wu, Z., Leahy, R.: An optimal graph theoretic approach to data clustering: Theory and applications to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11), 1101–1113 (1993)
Xu, Y., Olman, V., Uberbacher, E.C.: A segmentation algorithm for noisy images: Design and evaluation. Pattern Recognition Letters 19, 1213–1224 (1998)
Zahn, C.T.: Graph theoretical methods for detecting and describing gestalt clusters. IEEE Transactions on Computers 20(1), 68–86 (1971)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Amelio, A., Pizzuti, C. (2012). An Evolutionary and Graph-Based Method for Image Segmentation. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32937-1_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-32937-1_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32936-4
Online ISBN: 978-3-642-32937-1
eBook Packages: Computer ScienceComputer Science (R0)