Abstract
Image segmentation occupies the important position in image processing, so both high-efficiency and accurate segmentation are of great importance to image’s subsequent research. In this paper, taking the color and texture features into account, the color features are combined with RGB and HSV color space features, and the texture features are made up with the mean value and standard deviation. Considering the advantages of support vector machine (SVM) in classification, we convert the segmentation problem into a classification problem, and we can get the satisfied segmentation result. Genetic Algorithms (GA) is used for the optimization of the parameters in SVM’s kernel which is combined with polynomial (poly) and radial basis function (rbf) kernel reasonably by mercer theorem. The experimental results show that we can segment various kinds of color images effectively.
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
Wang, X.Y., Wang, Q.Y.: Color Image Segmentation Using Automatic Pixel Classification with Support Vector Machine. Neurocomputing, 3898–3911 (2011)
Yoon, K.-J., Kweon, I.-S.: Color Image Segmentation Considering Human Sensitivity for Color Pattern Variations. In: Proceedings of the International Society for Optical Engineering (SPIE), pp. 269–278 (2001)
Moreno, R., Grana, M.: Image Segmentation on Spherical Coordinate Representation of RGB Colour Space. Computational Intelligence Group, Universidad del Pais Vasco, IET image process 6, 1275–1283 (2012)
Zhang, J., Du., M.: Fast Image Segmentation of Gold Immunochromatographic Strip Based on Fcm Clustering Algorithm in HSV Color Space. Image and Signal Processing(CISP), 525–528 (2012)
Bo, H., Ma, F.: Analysis of Calculation Problem in Image Texture Gray level Co-occurrence Matrix. Journal of Electronics 34(1) (2006)
Deng, N., Tian, Y.: Support Vector Machine (SVM)- Theory, Algorithms, and Development, pp. 81–86. Science Press, Beijing (2009)
Lin, H.-T., Lin, C.-J.: A Study on Sigmod Kernels for SVM and the Training of Non-PSD Kernels by SMO-Type Methods, http://www.csie.ntu.edu.tw/~cjlin/papers/tanh.pdf
Gao, H.Z., Wan, J.W.: The Hyperspectral Image Classification Technology Research Based on Combined Kernel in Spectral and Air Space. Signal Processing 27(5) (2011)
Angelina, S., Padma Suresh, L., Krishna Veni, S.H.: Image Segmentation Based On Genetic Algorithm for Region Growth and Region Merging. In: 2012 International Conference on Computing, Electronics and Electrical Technologies, pp. 970–974 (2012)
Liu, G.H., Bao, H., Li, W.C.: Using Matlab to Realize Genetic Algorithm Program. Computer Application and Research, 80–82 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, L., Shi, D.y., Xu, J. (2013). Color Image Segmentation Based-on SVM Using Mixed Features and Combined Kernel. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_51
Download citation
DOI: https://doi.org/10.1007/978-3-642-42057-3_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42056-6
Online ISBN: 978-3-642-42057-3
eBook Packages: Computer ScienceComputer Science (R0)