Abstract
This paper presents a novel texture segmentation scheme based on two techniques: watershed and a novel structural adaptation artificial immune antibody competitive network (SAIANet). The proposed scheme first partitions image into a set of regions by watershed algorithm and then clusters the watershed regions by SAIANet, where the gray level co-occurrence matrix and the wavelet frame texture features are extracted from each watershed region as the antigens of SAIANet. A new immune antibody neighborhood and an adaptive learning coefficient are presented, and inspired by the long-term memory in cerebral cortices, a long-term memory coefficient is introduced into the network. The minimal spanning tree in graph theory is used to automatically cluster antibody obtained in the output space without a predefined number of clustering. Finally, the presented SAIANet is devoted to performing a fully unsupervised texture segmentation with a superior performance, which makes full use of the watershed segmentation results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Helder, K., de Castro, L.N., Von Zuben, F.J.: RABNET: A Real-Valued Antibody Network for Data Clustering. In: GECCO 2005, Washington, DC, USA, June 25-29, pp. 371–372 (2005)
Hunt, J.E., Cooke, D.E.: Learning Using an Artificial Immune System. Journal of Network and Computer Applications 19, 189–212 (1996)
Kohonen, T.: The self-organizing map. Neurocomput. 21, 1–6 (1998)
Lanprecht, R., LeDoux, J.: Structural plasticity and memory. A Nature Rev. Neuroscience 5, 45–54 (2004)
Vincent, L., Soille, P.: Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations. IEEE Transactions on PAMI 13(6), 583–598 (1991)
Wang, D.: A Multiscale Gradient Algorithm for Image Segmentation Using Watersheds. Pattern Recognition 30(12), 2043–2052 (1997)
Gotlieb, C.C., Kreyszig, H.E.: Texture descriptors based on cooccurrence matrices. Comput. Vision, Graph. Image Processing 51, 70–86 (1990)
Unser, M.: Texture classification and segmentation using wavelet frames. IEEE Trans. Image Process. 4(11), 1549–1560 (1995)
Laine, A., Fan, J.: Frame representation for texture segmentation. IEEE Trans. Image Process 5(5), 771–780 (1996)
Leclerc, B.: Minimum spanning trees for tree metrics: abridgements and adjustments. Journal of Classification 12, 207–241 (1995)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover Publication, New York (1966)
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
Huang, W., Jiao, L. (2006). Unsupervised Texture Segmentation Based on Watershed and a Novel Artificial Immune Antibody Competitive Network. In: Chang, LW., Lie, WN. (eds) Advances in Image and Video Technology. PSIVT 2006. Lecture Notes in Computer Science, vol 4319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949534_49
Download citation
DOI: https://doi.org/10.1007/11949534_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68297-4
Online ISBN: 978-3-540-68298-1
eBook Packages: Computer ScienceComputer Science (R0)