Abstract
In this work, a method is described for evolving adaptive procedures for colour image segmentation. We formulate the segmentation problem as an optimisation problem and adopt evolutionary strategy of Genetic Algorithms (GA) for the clustering of small regions in colour feature space. The present approach uses k-Means unsupervised clustering methods into GA, namely for guiding this last Evolutionary Algorithm in his search for finding the optimal or sub-optimal data partition, task that as we know, requires a non-trivial search because of its intrinsic NP-complete nature. To solve this task, the appropriate genetic coding is also discussed, since this is a key aspect in the implementation. Our purpose is to demonstrate the efficiency of GA to automatic and unsupervised texture segmentation. Some examples in Colour Maps are presented and overall results discussed
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
Andrey P., 1999, “Selectionist Relaxation: Genetic Algorithms applied to Image Segmentation”, Image and Vision Computing 17, 175–187.
Bhandarkar S.M., Zhang Y. and Potter W.D., 1994, “An Edge Detection Technique using Genetic Algorithm-based Optimisation”, Pattern Recognition 27(9), 1159-1180.
Bhanu B., Lee S., 1994, Genetic Learning for Adaptive Image Segmentation, KAP.
Bhanu B., Lee, S. and Ming, J., 1995, “Adaptive Image Segmentation using a Genetic Algorithm”, IEEE Transactions on Systems, Man, and Cybernetics 25(12), 1543–1567.
Bounsaythip C. and Alander J.T., 1997, “Genetic Algorithms in Image Processing-A Review”, Proc. of the 3rd Nordic Workshop on Genetic Algorithms and their Applications, Metsatalo, Univ. of Helsinki, Finland, 173–192.
Cagnoni S., Dobrzeniecki A.B., Poli R. & Yanch J.C., 1999, Genetic Algorithm-based Interactive Segmentation of 3D Medical Images, Image and Vision Computing 17, 881–895.
Chun D.N. and Yang., H.S., 1996, “Robust Image Segmentation using Genetic Algorithm with a Fuzzy Measure”, Pattern Recognition 29(7), 1195–1211.
Davis L.D., 1991, Handbook of Genetic Algorithms, Van Nostrand Reinhold, NYC.
Davis L.S. and Rosenfeld, A., 1981, “Cooperating Processes for Low-Level Vision: A Survey”, Artificial Intelligence 17, 245–263. ai[10]_Delibasis K., Undrill P.E., and Cameron G.G., 1997, “Genetic Algorithms applied to Fourier descriptor based Geometric Models for Anatomical Object Recognition in Medical Images”, Computer Vision and Image Understanding 66(9), 286–300.
Duda R. & Hart P., 1973, Pattern Classification and Scene Analysis, J.Wiley & Sons, NYC.
Falkenauer E., 1998, Genetic Algorithms and Grouping Problems, J. Wiley & Sons, Boston.
Goldberg D.E., 1989, Genetic Algorithms in Search, Optimisation and Machine Learning, Addison-Wesley. ai[14]_Koza J.R., 1992, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, Massachusetts.
Michalewicz Z., 1996, Genetic Algorithms + Data Structures = Evolution Programs, 3rd Ed., Springer-Verlag.
Pal S. & Wang P. (eds), 1996, Genetic Algorithms for Pattern Recognition, CRC Press.
Poli R. and Valli G., 1993, “Neural Inhabitants of MR and Echo Images Segment Cardiac Structures”, IEEE Computer Society Press, London, 193–196.
Poli R., 1996, “Genetic Programming for Image Analysis”, in Koza, J.R., Goldberg, D.E., Fogel, D.B. and Riolo, R.L. (Eds.), Genetic Programming 96, Proc. 1st Annual Conference, Stanford Univ., MIT Press, 363–368.
Ramos V., 1997, Evolution and Cognition in Image Analysis, MSc Thesis (in Portuguese), 230 pp., Instituto Superior Técnico-IST, Lisbon, December 1997.
Ramos V. & Almeida F., 2000, “Artificial Ant Colonies in Digital Image Habitats-A *Mass Behaviour Effect Study on Pattern Recognition”, accepted for ANTS’2000-2nd Int. Workshop on Ant Algorithms (From Ant Colonies to Artificial Ants), Brussels, Sep. 7–9.
Ramos, V. and Muge, F., 2000, “On Image Filtering, Noise and Morphological Size Intensity Diagrams”, Pattern Recognition Letters. in press, (also on RecPad’2000).
Shafer S. & Kanade T., 1982, “Recursive Region Segmentation by Analysis of Histograms”, Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, 1166–1171. ai[23]_Yoshimura M. and Oe S., 1999, “Evolutionary Segmentation of Texture Image using Genetic Algorithms towards Automatic Decision of Optimum Number of Segmentation Areas”, Pattern Recognition 32, 2041–2054.
Zhang Y.J. & Gerbrands, J.J., 1994, “Objective and Quantitative Segmentation Evaluation and Comparison”, Signal Processing 39, 43–54.
Zhang Y.J, 1996, “A Survey on Evaluating Methods for Image Segmentation”, Pattern Recognition 29(8), 1335–1246.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ramos, V., Muge, F. (2000). Map Segmentation by Colour Cube Genetic K-Mean Clustering. In: Borbinha, J., Baker, T. (eds) Research and Advanced Technology for Digital Libraries. ECDL 2000. Lecture Notes in Computer Science, vol 1923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45268-0_30
Download citation
DOI: https://doi.org/10.1007/3-540-45268-0_30
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41023-2
Online ISBN: 978-3-540-45268-3
eBook Packages: Springer Book Archive