Abstract
The image segmentation algorithm based on graph cut guarantees a globally optimal solution for energy solution, which is usually with the aid of user’s interactive operation. For the multi-label image segmentation application, the graph cut algorithm has two drawbacks. Firstly, it has a higher computational complexity of segment multi-label images. Secondly, it is prone to be trapped in local minima when solves the energy formulation. For the two drawbacks, this paper presents an auto-segmentation algorithm based on graph cut to segment multi-label images. The number of the labels is obtained via the main colors of the image, then the main colors are employed as pre-specified nodes feature, rather than select seeds with the aid of prior knowledge or initialization operation by the user. The seeds can be selected automatically without complex mathematical formulations to computerize, and it reduces the computational complexity successfully, and avoids falling into local minima effectively. In additional, we use a fast α-expansion move algorithm to optimize the energy function, which can improve the speed of segmentation. Comparing the proposed algorithm with the state-of-the-art segmentation methods, the experimental results show that the proposed algorithm has superior performance.
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Wang, J., Jia, Y., Hua, X.-S., Zhang, C., & Quan, L. (2008). Normalized tree partitioning for image segmentation. In IEEE conference on computer vision and pattern recognition, CVPR 2008 (pp. 1–8).
Muir, A., Dormido, R., Duro, N., et al. (2016). Determination of the optimal number of clusters using a spectral clustering optimization. Expert Systems with Applications An International Journal, 65C, 304–314.
Bayá, A. E., Larese, M. G., & Namías, R. (2017). Clustering stability for automated color image segmentation. Expert Systems with Applications, 86, 258–273.
Boykov, Y., & Jolly, M. (2001) Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images, volume 1. In International conference on computer vision (ICCV), Vancouver, British Columbia, Canada, 2001 (pp. 105–112).
Slabaugh, G., Unal, G. (2005). Graph cuts segmentation using an elliptical shape prior, volume 2. In IEEE international conference on image processing (ICIP), Genoa, Italy (pp. 1222–1225).
Zagrouba, E., Gamra, S. B., & Najjar, A. (2014). Model-based graph-cut method for automatic flower segmentation with spatial constraints. Image and Vision Computing, 32, 1007–1020.
Yu, Z., Xu, M., & Gao, Z. (2011). Biomedical image segmentation via constrained graph cuts and pre-segmentation. In International conference of the IEEE on EMBC (pp. 5714–5717).
Peng, Y., & Liu, R. (2010). Object segmentation based on watershed and graph cut. In International congress on CISP (pp. 1431–1435).
Lempitsky, V., Kohli, P., Rother, C., Sharp, T. (2010). Image segmentation with a bounding box prior. In IEEE conference on computer vision (pp. 277–284).
Yangel, B., & Vetrov, D. (2011) Image segmentation with a shape prior based on simplified skeleton. In EMMCVPR’11: Energy minimization methods in computer vision and pattern recognition, St. Petersburg, Russia (pp. 247–260).
Delong, A., Osokin, A., Isack, H. N., & Boykov, Y. (2012). Fast approximate energy minimization with label costs. International Journal of Computer Vision, 96(1), 1–27.
Boykov, Y., Veksler, O., & Zabih, R. (2001). Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Analysis and Machine Intelligence, 23(11), 1222–1239.
Lempitsky, V., Kohli, P., Rother, C., & Sharp, T. (2010). Image segmentation with a bounding box prior. In IEEE conference on computer vision (pp. 277–284).
Boykov, Y., & Kolmogorov, V. (2004). An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9), 1124–1137.
Zheng, Y., & Chen, P. (2013). Clustering based on enhanced α-expansion move. IEEE Transactions on Knowledge and Data Engineering, 25(25), 2206–2216.
Bi, A., Chung, F. I., Wang, S., Jiang, Y., & Huang, Ch. (2016). Bayesian enhanced α-expansion move clustering with loose link constraints. Neurocomputing, 194, 288–300.
Sashida, S., Okabe, Y., & Lee, H. K. (2014). Comparison of multi-label graph cuts method and Monte Carlo simulation with block-spin transformation for the piecewise constant Mumford-Shah segmentation model. Computer Vision and Image Understanding, 119, 15–26.
Watanabe, H., Sashida, S., Okabe, Y., & Lee, H. (2011). Monte Carlo methods for optimizing the piecewise constant Mumford-Shah segmentation model. New Journal of Physics, 13, 4–23.
Al-Shaikhli, S. D. S., Yang, M., & Rosenhahn, B. (2014). Multi-region labeling and segmentation using a graph topology prior and atlas information in brain images. Computerized Medical Imaging and Graphics, 38, 725–734.
Al-Shaikhli, S. D. S., Yang, M., Rosenhahn, B. (2013). Medical image segmentation using multi-level set partitioning with topological graph prior. In Pacific-rim symposium on image and video technology PSIVT workshops, LNCS (Vol. 8334, pp 157–168).
https://www.microsoft.com/en-us/research/project/image-understanding.
Rother, C., Kolmogorov, V., & Blake, A. (2004). Grabcut: Interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics, 23(3), 309–314.
Grady, L. (2006). Random walks for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(11), 1768–1783.
Cousty, J., Bertrand, G., Najman, L., & Couprie, M. (2009). Watershed cuts: Minimum spanning forests and the drop of water principle. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(8), 1362–1374.
Couprie, C., Grady, L., Najman, L., & Talbot, H. (2011). Power watershed: A unifying graph-based optimization framework. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(7), 1384–1399.
Casaca1, W., Nonato1, L. G., & Taubin, G. (2014) Laplacian coordinates for seeded image segmentation. In IEEE CVPR (pp. 1–8).
Arbelaez, P., Maire, M., Fowlkes, C., & Malik, J. (2011). Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(5), 898–916.
Unnikrishnan, R., Pantofaru, C., & Hebert, M. (2007). Toward objective evaluation of image segmentation algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), 929–944.
Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2001). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings of 8th international conference computer vision (ICCV) (Vol. 2, pp. 416–423).
Meila, M. (2005). Comparing clusterings: An axiomatic view. In Proceedings of the 22nd international conference on machine learning, ICML’05 (pp. 577–584). New York: ACM.
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This research is supported by Collaborative innovation project of green printing and publishing technology of Beijing Municipal Education Commission: PXM2016-014223-0000025 and Scientific Research Project of Beijing Education Committee (KM201710015010).
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Qi, Y., Zhang, G. & Li, Y. An Auto-Segmentation Algorithm for Multi-Label Image Based on Graph Cut. Sens Imaging 19, 13 (2018). https://doi.org/10.1007/s11220-018-0193-z
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DOI: https://doi.org/10.1007/s11220-018-0193-z