Image Segmentation Based on an Improved Fuzzy Clustering Algorithm
محورهای موضوعی : Computer Engineering
1 -
کلید واژه: computer vision, Image Segmentation, Fuzzy Clustering,
چکیده مقاله :
|
Traditional fuzzy clustering algorithms are considered powerful tools for image segmentation. However, these algorithms face two main challenges. First, they are sensitive to outliers. The fuzzy memberships in these algorithms are non-dispersive, meaning they are heavily influenced by outliers, largely due to the use of squared error in their objective function. This flaw can lead to incorrect and unreliable clustering results, reducing robustness. Second, they tend to produce an excessive number of clusters. Traditional fuzzy clustering algorithms often create too many clusters, many of which are unnecessary and redundant. This phenomenon, known as over-segmentation in fuzzy clustering, occurs due to the image's loss of local spatial information. To address these challenges, this study presents a solution that enhances the robustness of the fuzzy clustering algorithm. The proposed algorithm includes two main components: the first involves adding a Gaussian-based regularizer to the objective function, which incorporates a Gaussian sub-criterion to calculate the distance between data points and cluster centres. By adding this criterion, the proposed method increases the dispersion of fuzzy membership functions, thereby reducing the impact of outliers and improving clustering accuracy. The second component involves using a filter to resolve the problem of excessive clustering. The proposed algorithm was compared with traditional fuzzy clustering methods and spatial information-based methods to validate its performance, yielding superior results. The algorithm achieves higher accuracy and cohesion in image segmentation while being more robust to outliers and noise.
|
[1] M. Chen, Y. Tang, X. Zou, K. Huang, L. Li, and Y. He, “High-accuracy multi-camera reconstruction enhanced by adaptive point cloud correction algorithm,” Opt. Lasers Eng., vol. 122, pp. 170–183, 2019, doi: 10.1016/j.optlaseng.2019.06.011.
[2] P. Rathore, J. C. Bezdek, S. M. Erfani, S. Rajasegarar, and M. Palaniswami, “Ensemble Fuzzy Clustering Using Cumulative Aggregation on Random Projections,” IEEE Trans. Fuzzy Syst., vol. 26, no. 3, pp. 1510–1524, 2018, doi: 10.1109/TFUZZ.2017.2729501.
[3] K. Chen and X. Chen, “Fuzzy C-Means Clustering Image Segmentation Algorithm with Local Spatial Information Based on ELM,” Shuju Caiji Yu Chuli/Journal Data Acquis. Process., vol. 34, no. 1, pp. 100–110, 2019, doi: 10.16337/j.1004-9037.2019.01.011.
[4] A. Sabbaghi, M. R. Keyvanpour, and S. Parsa, “FCCI: A fuzzy expert system for identifying coincidental correct test cases,” J. Syst. Softw., vol. 168, 2020, doi: 10.1016/j.jss.2020.110635.
[5] Z. Li, K. Kamnitsas, and B. Glocker, “Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentation,” IEEE Trans. Med. Imaging, vol. 40, no. 3, pp. 1065–1077, 2021, doi: 10.1109/TMI.2020.3046692.
[6] M. Diwakar and M. Kumar, “A review on CT image noise and its denoising,” Biomed. Signal Process. Control, vol. 42, pp. 73–88, 2018, doi: 10.1016/j.bspc.2018.01.010.
[7] F. Zhang, H. Liu, C. Cao, Q. Cai, and D. Zhang, “RVLSM: Robust variational level set method for image segmentation with intensity inhomogeneity and high noise,” Inf. Sci. (Ny)., vol. 596, pp. 439–459, 2022, doi: 10.1016/j.ins.2022.03.035.
[8] J. Cai, S. Gu, and L. Zhang, “Learning a deep single image contrast enhancer from multi-exposure images,” IEEE Trans. Image Process., vol. 27, no. 4, pp. 2049–2062, 2018, doi: 10.1109/TIP.2018.2794218.
[9] B. Sasmal and K. G. Dhal, “A survey on the utilization of Superpixel image for clustering based image segmentation,” Multimed. Tools Appl., vol. 82, no. 23, pp. 35493–35555, 2023, doi: 10.1007/s11042-023-14861-9.
[10] P. Peng et al., “Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation,” Interdiscip. Sci. – Comput. Life Sci., vol. 16, no. 1, pp. 39–57, 2024, doi: 10.1007/s12539-023-00580-0.
[11] K. G. Dhal, A. Das, S. Ray, J. Gálvez, and S. Das, “Nature-Inspired Optimization Algorithms and Their Application in Multi-Thresholding Image Segmentation,” Arch. Comput. Methods Eng., vol. 27, no. 3, pp. 855–888, Jul. 2020, doi: 10.1007/s11831-019-09334-y.
[12] H. Y. Yalic and A. B. Can, “Automatic object segmentation on RGB-D data using surface normals and region similarity,” VISIGRAPP 2018 - Proc. 13th Int. Jt. Conf. Comput. Vision, Imaging Comput. Graph. Theory Appl., vol. 4, pp. 379–386, 2018, doi: 10.5220/0006617303790386.
[13] H. Zhang, Q. Wang, W. Shi, and M. Hao, “A novel adaptive fuzzy local information C-means clustering algorithm for remotely sensed imagery classification,” IEEE Trans. Geosci. Remote Sens., vol. 55, no. 9, pp. 5057–5068, 2017, doi: 10.1109/TGRS.2017.2702061.
[14] Y. Zhang, X. Bai, R. Fan, and Z. Wang, “Deviation-sparse fuzzy C-means with neighbor information constraint,” IEEE Trans. Fuzzy Syst., vol. 27, no. 1, pp. 185–199, 2019, doi: 10.1109/TFUZZ.2018.2883033.
[15] A. Sabbaghi, H. Rashidy Kanan, and M. R. Keyvanpour, “FSCT: A new fuzzy search strategy in concolic testing,” Inf. Softw. Technol., vol. 107, pp. 137–158, 2019, doi: 10.1016/j.infsof.2018.11.006.
[16] M. Gholizade, M. Rahmanimanesh, H. Soltanizadeh, and S. S. Sana, “Hesitant triangular fuzzy FlowSort method: the multi-criteria decision-making problems,” Int. J. Syst. Sci. Oper. Logist., vol. 10, no. 1, 2023, doi: 10.1080/23302674.2023.2259293.
[17] M. Gong, Y. Liang, J. Shi, W. Ma, and J. Ma, “Fuzzy C-means clustering with local information and kernel metric for image segmentation,” IEEE Trans. Image Process., vol. 22, no. 2, pp. 573–584, 2013, doi: 10.1109/TIP.2012.2219547.
[18] H.-J. Xing and M.-H. Ha, “Further improvements in Feature-Weighted Fuzzy C-Means,” Inf. Sci. (Ny)., vol. 267, pp. 1–15, May 2014, doi: 10.1016/j.ins.2014.01.033.
[19] D. Hallac, J. Leskovec, and S. Boyd, “Network lasso: Clustering and optimization in large graphs,” Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., vol. 2015-Augus, pp. 387–396, 2015, doi: 10.1145/2783258.2783313.
[20] L. Guo, L. Chen, X. Lu, and C. L. P. Chen, “Membership affinity lasso for fuzzy clustering,” IEEE Trans. Fuzzy Syst., vol. 28, no. 2, pp. 294–307, 2020, doi: 10.1109/TFUZZ.2019.2905114.
[21] E. Ghadimi, A. Teixeira, I. Shames, and M. Johansson, “Optimal parameter selection for the Alternating Direction Method of Multipliers (ADMM): Quadratic problems,” IEEE Trans. Automat. Contr., vol. 60, no. 3, pp. 644–658, 2015, doi: 10.1109/TAC.2014.2354892.
[22] N. R. Pal, K. Pal, J. M. Keller, and J. C. Bezdek, “A possibilistic fuzzy c-means clustering algorithm,” IEEE Trans. Fuzzy Syst., vol. 13, no. 4, pp. 517–530, 2005, doi: 10.1109/TFUZZ.2004.840099.
[23] T. Lei, X. Jia, Y. Zhang, S. Liu, H. Meng, and A. K. Nandi, “Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation,” IEEE Trans. Fuzzy Syst., vol. 27, no. 9, pp. 1753–1766, Sep. 2019, doi: 10.1109/TFUZZ.2018.2889018.
[24] S. Zhou, D. Li, Z. Zhang, and R. Ping, “A New Membership Scaling Fuzzy C-Means Clustering Algorithm,” IEEE Trans. Fuzzy Syst., vol. 29, no. 9, pp. 2810–2818, Sep. 2021, doi: 10.1109/TFUZZ.2020.3003441.
[25] T. Lei, X. Jia, Y. Zhang, L. He, H. Meng, and A. K. Nandi, “Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering,” IEEE Trans. Fuzzy Syst., vol. 26, no. 5, pp. 3027–3041, 2018, doi: 10.1109/TFUZZ.2018.2796074.
[26] K. D. Koutroumbas, S. D. Xenaki, and A. A. Rontogiannis, “On the Convergence of the Sparse Possibilistic C-Means Algorithm,” IEEE Trans. Fuzzy Syst., vol. 26, no. 1, pp. 324–337, 2018, doi: 10.1109/TFUZZ.2017.2659739.
[27] S. Chen and D. Zhang, “Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. 34, no. 4, pp. 1907–1916, 2004, doi: 10.1109/TSMCB.2004.831165.
[28] A. Sodiqin, “Fuzzy C-means as a regularization and maximum entropy approach,” J. Chem. Inf. Model., vol. 53, no. 9, pp. 1–21, 2013.
[29] J. Huang, F. Nie, and H. Huang, “A new simplex sparse learning model to measure data similarity for clustering,” IJCAI Int. Jt. Conf. Artif. Intell., vol. 2015-Janua, pp. 3569–3575, 2015.
[30] G. Liu, Y. Zhang, and A. Wang, “Incorporating adaptive local information into fuzzy clustering for image segmentation,” IEEE Trans. Image Process., vol. 24, no. 11, pp. 3990–4000, 2015, doi: 10.1109/TIP.2015.2456505.
