Abstract
A novel scalable graph partitioning framework has been proposed in this paper. The scalable graph partitioning is new thought to deal with the large scale images, which improves the efficiency greatly and maintains the major local details. It involves two levels clustering, namely blockwise and segment, to achieve the excellent performance. In this paper, spectral clustering has been implemented twice combined with the morphologic adjacencies separating and merging algorithm to obtain the final segmentation results. Experimental results show that it keeps fine details and removes the noise pixels generated by spectral clustering.
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.
References
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)
Yu, S.X., Shi, J.: Segmentation given partial grouping constraints. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(2), 173–183 (2004)
Meila, M., Xu, L.: Multiway cuts and spectral clustering, Advances in Neural Information Processing Systems (2003)
Ding, C., He, X.H, Zha, Gu, M., Simon, H.: A min-max cut algorithm for graph partitioning and data clustering. In: IEEE first Conference on Data Mining, pp. 107–114 (2001)
Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: Advances in Neural Information Processing Systems, vol. 14, pp. 849–856 (2002)
Fowlkes, C., Belongie, S., Chung, F., Malik, J.: Spectral Grouping Using the Nyström Method. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(2), 214–225 (2004)
Xiang, T., Gong, S.: Spectral clustering with eigenvector selection. Pattern Recognition 41(3), 1012–1029 (2008)
Tang, F., Wong, A., Clausi, D.A.: Enabling scalable spectral clustering for image segmentation. Pattern Recognition 43(1), 4069–4076 (2010)
Canny, J.: A computational approach to edge detection. PAMI(1986)
von Luxburg, U.: A tutorial on spectral clustering, Technical report, No TR-149, Max-Planck-Institut für biologische Kybernetik (2007)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5), 564–575 (2003)
Ostu, N.: Threshold selection method from gray-level histogram. IEEE Trans. SMC9(1), 62–66 (1979)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of IEEE International Conference on Computer Vision, vol. 2, pp. 416–423 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
You, L., Zhou, S., Gao, G., Leng, M. (2011). Scalable Spectral Clustering Combined with Adjacencies Merging for Image Segmentation. In: Wu, Y. (eds) Advances in Computer, Communication, Control and Automation. Lecture Notes in Electrical Engineering, vol 121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25541-0_89
Download citation
DOI: https://doi.org/10.1007/978-3-642-25541-0_89
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25540-3
Online ISBN: 978-3-642-25541-0
eBook Packages: EngineeringEngineering (R0)