Abstract
Image segmentation methods based on spectral clustering overcome some drawbacks of the so-called central-grouping. Nyström is one of them, which uses only a partial smaller set of samples to replace the whole image pixels. In order to utilize the region information and select the sample set of the image, an image segmentation algorithm based on superpixel and improved Nyström algorithm is proposed in this paper. Firstly, region information is obtained by the superpixel method. Then the similarity measure for regions is constructed. Finally, an interval sample strategy is designed in Nyström and the regions are clustered to create the image segmentation result. With this method, the instability of random sampling is overcome, and the time complexity of color image segmentation is reduced. This method is applied to some images selected from Berkeley and VOC segmentation images. Experimental results show that our method has more advantages than FCM and Nyström algorithm in segmenting images.
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
Similar content being viewed by others
References
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Massachusetts (1992)
Chen, S.C., Zhang, D.Q.: Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans., Syst., Man, Cybern., Part B: (Cybernetics) 34(4), 1907–1916 (2004)
Pal, N.R., Pal, S.K.: A review on image segmentation to techniques. Pattern Recognit. 26(9), 1277–1294 (1993)
Cheriet, M., Said, J.N., Suen, C.Y.: A recursive thresholding technique for image segmentation. IEEE Trans. on Image Process. 7(6), 918–921 (1998)
Frank, Y.S., Cheng, S.: Automatic seeded region growing for color image segmentation. Image Vis. Comput. 23(10), 877–886 (2005)
Yang, X., Krishnan, S.M.: Image segmentation using finite mixtures and spatial information. Image Vis. Comput. 22(9), 735–745 (2004)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
Fowlkes, C., Belongie, S., Chung, F., Malik, J.: Spectral grouping using the Nyström. IEEE Trans. on Pattern Anal. Mach. Intell. 26(2), 214–225 (2004)
Garcia, J.F.G., Andraca, S.E.V.: Region-based approach for the spectra clustering Nyström approximation with an application to burn depth assessment. Mach. Vis. Appl. 26(2), 353–368 (2015)
Achanta, R., Kevin, S.A.S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. on Pattern Anal. Mach. Intell. 34(11), 2274–2281 (2012)
Ayvaci, A., Soatto, S.: Motion Segmentation with Occlusions on the Superpixel Graph. In: 12th International Conference Computer Vision, pp. 727-734. Kyoto (2009)
Veksler, O., Bpykov, Y., Mehrani, P.: Superpixels and Supervoxels in an Energy Optimization Framework. In: 11th European Conference on Computer Vision, pp:211-224. Springer, Berlin, Heidelberg (2010)
Comanciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. on Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)
Yang, Y., Hallman, S., Ramanan, D. and Fawlkes, C.: Layered Object Detection for Multi-class Segmentation. In: Computer Vision and Pattern Recognition (CVPR), pp. 3113–3120. (2010)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant Nos. 61102095, 61202153 and 61571361), the Science and Technology Plan in Shaanxi Province of China (Grant No. 2014KJXX-72), the Fundamental Research Funds for the Central Universities (Grant No. GK201503063).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhao, J., Liu, HQ., Zhao, F. (2017). Color Image Segmentation Based on Superpixel and Improved Nyström Algorithm. In: Fan, TH., Chen, SL., Wang, SM., Li, YM. (eds) Quantitative Logic and Soft Computing 2016. Advances in Intelligent Systems and Computing, vol 510. Springer, Cham. https://doi.org/10.1007/978-3-319-46206-6_56
Download citation
DOI: https://doi.org/10.1007/978-3-319-46206-6_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46205-9
Online ISBN: 978-3-319-46206-6
eBook Packages: EngineeringEngineering (R0)