Abstract
With the recent advances in complex networks, image segmentation becomes one of the most appropriate application areas. In this context, we propose in this paper a new perspective of image segmentation by applying two efficient community detection algorithms. By considering regions as communities, these methods can give an over-segmented image that has many small regions. So, the proposed algorithms are improved to automatically merge those neighboring regions agglomerative to achieve the highest modularity/stability. To produce sizable regions and detect homogeneous communities, we use the combination of a feature based on the Histogram of Oriented Gradients of the image, and feature based on color to characterize the similarity of two regions. By constructing the similarity matrix in an adaptive manner, we avoid the problem of the over-segmentation. We evaluate the proposed algorithms for Berkeley Segmentation Dataset, and we show that our experimental results can outperform other segmentation methods in terms of accuracy and can achieve much better segmentation results.
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References
J. A. Noble and D. Boukerroui, “Ultrasound image segmentation: A survey,” IEEE Transactions on medical imaging, 25(8), 987-1010.
H. Zhang, J. E. Fritts, and S. A. Goldman, “Image segmentation evaluation: A survey of unsupervised methods,” Computer Vision and Image Understanding, Elsevier, vol. 110, no. 2, pp. 260-280, 2008.
A. M. Khan, Ravi. S, “Image Segmentation Methods: A Comparative Study”, IJSCE, Volume-3, Issue-4, September 2013.
L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 6, pp. 583-598, 1991.
V. Grau, A. Mewes, M. Alcaniz, R. Kikinis, and S. K. Warfield, “Improved watershed transform for medical image segmentation using prior information,” IEEE Transactions on Medical Imaging, vol. 23, no. 4, pp. 447-458, 2004.
X.-C. Tai, E. Hodneland, J. Weickert, N. V. Bukoreshtliev, A. Lundervold, and H.-H. Gerdes, “Level set methods for watershed image segmentation,” in Scale Space and Variational Methods in Computer Vision. Springer, 2007, pp. 178-190.
V. Osma-Ruiz, J. I. Godino-Llorente, N. Saenz-Lech on, and P. G omez-Vilda, “An improved watershed algorithm based on efficient computation of shortest paths,” Pattern Recognition, vol. 40, no. 3, pp. 1078-1090, 2007.
R.Yogamangalam, B.Karthikeyan, “Segmentation Techniques Comparison in Image Processing”, IJET, Vol 5, No 1, Feb-Mar 2013.
D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 7, pp. 1271-1283, 2010.
L. Angelini, D. Marinazzo, M. Pellicoro, and S. Stramaglia, “Natural clustering: the modularity approach,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2007, no. 08, p. L08001, 2007.
J.-C. Delvenne, S. N. Yaliraki, and M. Barahona, “Stability of graph communities across time scales,” Proceedings of the National Academy of Sciences, vol. 107, no. 29, pp. 12 755-12760, 2010.
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR., vol. 1. IEEE, 2005, pp. 886-893.
M. A. Porter, J.-P. Onnela, and P. J. Mucha, “Communities in networks,” Notices of the AMS, vol. 56, no. 9, pp. 1082-1097, 2009.
M. E. Newman and M. Girvan, “Finding and evaluating community structure in networks,” Physical review E, vol. 69, no. 2, p. 026113, 2004.
M. E. Newman, “Analysis of weighted networks,” Physical Review E, vol. 70, no. 5, p. 056131, 2004.
Le Martelot, Erwan, and Chris Hankin. “Multi-scale community detection using stability optimisation within greedy algorithms.” arXiv preprint arXiv:1201.3307(2012).
L. Danon, A. Diaz-Guilera, A. Arenas, “The effect of size heterogeneity on community identification in complex networks”, J. Stat. Mech. 11 (2006).
Mourchid, Y., El Hassouni, M., and Cherifi, H. “Image segmentation based on community detection approach”. The International Journal of Computer Information Systems and Industrial Management Applications. ISSN 2150-7988 Volume 8 (2016) pp. 195-204
P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, “Contour detection and hierarchical image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 5, pp. 898-916, 2011.
A. Yang, J. Wright, Y. Ma, and S. Sastry, “Unsupervised segmentation of natural images via lossy data compression,” Computer Vision and Image Understanding, vol. 110, no. 2, pp. 212-225, 2008.
C. M. Christoudias, B. Georgescu, and P. Meer, “Synergism in low level vision,” in Pattern Recognition, 2002. Proceedings. 16th International Conference on, vol. 4. IEEE, 2002, pp. 150-155.
Y. Deng and B. Manjunath, “Unsupervised segmentation of color-texture regions in images and video,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 23, no. 8, pp. 800-810, 2001.
R. Unnikrishnan, C. Pantofaru, and M. Hebert, “Toward objective evaluation of image segmentation algorithms,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 29, no. 6, pp. 929-944, 2007
R. Unnikrishnan, C. Pantofaru, and M. Hebert, “Toward objective evaluation of image segmentation algorithms,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 29, no. 6, pp. 929-944, 2007
G. Mori, “Guiding model search using segmentation,” in The Proceedings of the 10th IEEE International Conference on Computer Vision, ICCV., vol. 2. IEEE, 2005, pp. 1417-1423.
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Mourchid, Y., El Hassouni, M., Cherifi, H. (2017). An Image Segmentation Algorithm based on Community Detection. In: Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A. (eds) Complex Networks & Their Applications V. COMPLEX NETWORKS 2016 2016. Studies in Computational Intelligence, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-50901-3_65
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DOI: https://doi.org/10.1007/978-3-319-50901-3_65
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