Open Access Open Access  Restricted Access Subscription or Fee Access

Segmentation of Images using Automatic Fuzzy Clustering Framework

Sane Indra Kiran, Sangem Ravi Teja, Bairi Pavan Kumar, S. Ramani

Abstract


Clustering is the process of grouping data points together. The data points may be organised into groups with comparable attributes using clustering methods. Data points from one or more groups are grouped together in fuzzy clustering. Density Peak (DP) clustering may locate clusters, however as the number of clusters grows, memory overflow occurs, since when a normal-sized picture with a large number of pixels is used for image segmentation, it produces a high degree of similarity matrix. Automatic Fuzzy Clustering Framework (AFCF) for picture segmentation might be created to avoid this. This framework makes a three-fold contribution. To begin, the Density Peak approach is utilised to implement the notion of Super Pixel, which reduces the size of the similarity matrix and so improves the DP algorithm. Second, the Density Balance approach creates a robust decision network, allowing the DP algorithm to cluster data independently. Finally, the system that uses prior entropy applies Fuzzy c-means clustering to improve picture segmentation results. This takes into account the information of pixels in spatial neighbours, resulting in better segmentation outcomes. The goal of this paper is to create and explain picture segmentation using the Automatic Fuzzy Clustering Framework.


Full Text:

PDF

References


T. Lei, P. Liu, X. Jia, X. Zhang, H. Meng and A. K. Nandi, "Automatic Fuzzy Clustering Framework for Image Segmentation," in IEEE Transactions on Fuzzy Systems, vol. 28, no. 9, pp. 2078-2092, 2020.

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," in IEEE Transactions on Fuzzy Systems, vol. 27, no. 9, pp. 1753-1766, 2019.

Z. Ban, J. Liu, and L. Cao, “Superpixel segmentation using gaussian mixture model,” IEEE Transactions on Image Processing, vol. 27, no. 8, pp. 4105–4117, 2018.

A. Fahad, N. Alshatri, and Z. Tari, “A survey of clustering algorithms for big data: Taxonomy and empirical analysis,” IEEE Transactions on Emerging Topics in Computing, vol. 2, no. 3, pp. 267–279, 2014.

M. T. Law, R. Urtasun, and R. S. Zemel, “Deep spectral clustering learning,” in Proceedings of International Conference on Machine Learning , Sydney, Australia, 2017, pp. 1985–1994.

G. Nebehay and R. Pflugfelder, “Clustering of static- adaptive correspondences for deformable object tracking,” in Proc. IEEE Conference on Computing Vision Pattern Recognition, Boston, MA, USA, 2015, pp. 2784–2791.

G. Dong and M. Xie, “Color clustering and learning for image segmentation based on neural networks,” IEEE Transactions on Neural Network, vol. 16, no. 4, pp. 925–936, 2005.

N. Kumar, P. Uppala, and K. Duddu, “Hyperspectral tissue image segmentation using semi-supervised NMF and hierarchical clustering,” IEEE Transactions on Medical Imaging, vol. 38, no. 5, pp. 1304–1313, 2019.

K. Zhang, L. Zhang, K. M. Lam, and D. Zhang, “A level set approach to image segmentation with intensity inhomogeneity,” IEEE Transactions on Cybernetics, vol. 46, no. 2, pp. 546–557, 2016.

J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888–905, 2000.

L. Grady, “Random walks for image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 11, pp. 1452–1458, 2004.

M. N. Ahmed, S. M. Yamany, N. A. Mohamed, A. A. Farag, and T. Moriarty, “A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data,” IEEE Transactions on Medical Imaging, vol. 21, no. 3, pp. 193–199, 2002.

S. Chen and D. Zhang, “Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure,” IEEE Transactions on Systems, Man and Cybernetics, vol. 34, no. 4, pp. 1907–1916, 2004.

S. Krinidis and V.Chatzis, “A robust fuzzy local information c-means clustering algorithm,” IEEE Transactions on Image Processing, vol. 19, no. 5, pp. 1328– 1337, 2010.

Z. Zhao, L. Cheng, and G. Cheng, “Neighbourhood weighted fuzzy means clustering algorithm for image segmentation,” IET Image Processing, vol. 8, no. 3, pp. 150– 161, 2014.

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 Transactions on Image Processing, vol. 22, no. 2, pp. 573–584, 2013.

Y. Zhang, X. Bai, R. Fan, and Z. Wang, “Deviation-sparse fuzzy c-means with neighbour information constraint,” IEEE Transactions on Fuzzy Systems, vol. 27, no. 1, pp. 185–199, 2019.

G. Liu, Y. Zhang, and A. Wang, “Incorporating adaptive local information into fuzzy clustering for image segmentation,” IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3990–4000, 2015.


Refbacks

  • There are currently no refbacks.