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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 1/2020

15.02.2019 | Theoretical advances

Automatic grayscale image segmentation based on Affinity Propagation clustering

verfasst von: Shibing Zhou, Zhenyuan Xu

Erschienen in: Pattern Analysis and Applications | Ausgabe 1/2020

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Abstract

Image segmentation is an important research subject in the field of image analysis and pattern recognition. Based on the affinity propagation (AP) clustering algorithm, an automatic segmentation method is proposed for grayscale images. The AP algorithm is used for image segmentation to avoid the choice of initial clustering centers and enhance the stability of the segmentation results, and a new index is proposed to analyze the validity of segmentation results and determine the optimal number of segments. Moreover, gray values instead of pixels in gray space are clustered as the dataset to decrease the time complexity of the similarity matrix and validity analysis. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed index and method.
Vorheriger Artikel Weighted feature-task-aware regularization learner for multitask learning

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Literatur
1.
Jiang F, Grigorev A, Rho S et al (2018) Medical image semantic segmentation based on deep learning. Neural Comput Appl 29(5):1257–1265CrossRef
2.
Zhang X (2017) Melanoma segmentation based on deep learning. Comput Assist Surg 22(S1):267–277CrossRef
3.
Wang G et al (2018) Interactive medical image segmentation using deep learning with image-specific fine-tuning. IEEE Trans Med Imaging 37(7):1562–1573CrossRef
4.
Dunn JC (1973) A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J Cybern 3(3):32–57MathSciNetMATHCrossRef
5.
MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp 281–297
6.
Das S, Sil S (2010) Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm. Inf Sci 180(8):1237–1256MathSciNetCrossRef
7.
Huang Y, Fu K, Wu Y (2009) Image segmentation method using K-Means based on Markov random field. Acta Electron Sini 37(12):2700–2704
8.
9.
Yang X, Zhao W, Chen Y, Fang X (2008) Image segmentation with a fuzzy clustering algorithm based on ant-tree. Signal Process 88(10):2453–2462MATHCrossRef
10.
Tung F, Wong A, Clausi DA (2010) Enabling scalable spectral clustering for image segmentation. Pattern Recognit 43(12):4069–4076MATHCrossRef
11.
Sulaiman SN, Isa NAM (2010) Adaptive fuzzy-K-means clustering algorithm for image segmentation. IEEE Trans Consum Electron 56(4):2661–2668CrossRef
12.
Wang X, Bu J (2010) A fast and robust image segmentation using FCM with spatial information. Digit Signal Proc 20(4):1173–1182CrossRef
13.
Lu Q, Chen W (2006) Image segmentation based on mutual information. Chin J Comput 29(2):296–301MathSciNet
14.
Zhang X, Qian X, Jiao L (2010) Immune spectral clustering algorithm for image segmentation. J Softw 21(9):2196–2205MATH
15.
Tian J, Huang Y, Yu Y (2008) A fast FCM cluster multi-threshold image segmentation algorithm based on entropy constraint. PR & AI 21(2):221–226
16.
Liu Y, Lin B (2010) Fuzzy clustering image segmentation algorithm with high validity optimized by artificial immune algorithm. Control Decis 25(11):1679–1683
17.
Tang Y, Pan Z, Tang M, Heng P, Xia D (2009) Image segmentation with hierarchical mean shift. J Comput Res Dev 46(9):1424–1431
18.
Gao X, Li J, Ji H (2004) A multi-threshold image segmentation algorithm based on weighting fuzzy c-means clustering and statistical test. Acta Electron Sin 32(4):661–664
19.
Kim S, Kang M (2012) Multiple-region segmentation without supervision by adaptive global maximum clustering. IEEE Trans Image Process 21(4):1600–1612MathSciNetMATHCrossRef
20.
Gong M, Liang Y, Shi J, Ma W, Ma J (2013) Fuzzy C-Means clustering with local information and kernel metric for image segmentation. IEEE Trans Image Process 22(2):573–584MathSciNetMATHCrossRef
21.
Liu G, Zhang Y, Wang A (2015) Incorporating adaptive local information into fuzzy clustering for image segmentation. IEEE Trans Image Process 24(11):3990–4000MathSciNetMATHCrossRef
22.
Mitra S, Kundu PP (2011) Satellite image segmentation with shadowed C-means. Inf Sci 181(17):3601–3613CrossRef
23.
Zhao F, Liu H, Fan J (2015) A multiobjective spatial fuzzy clustering algorithm for image segmentation. Appl Soft Comput 30:48–57CrossRef
24.
Aparajeeta J, Nanda PK, Das N (2016) Modified possibilistic fuzzy C-means algorithms for segmentation of magnetic resonance image. Appl Soft Comput 41:104–119CrossRef
25.
Vishnuvarthanan G, Rajasekaran MP, Subbaraj P, Vishnuvarthanan A (2016) An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images. Appl Soft Comput 38:190–212CrossRef
26.
Ortiz A, Gorriz JM, Ramirez J, Salas-Gonzalez D (2014) Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering. Inf Sci 262:117–136CrossRef
27.
Li Z, Liu G, Zhang D, Xu Y (2016) Robust single-object image segmentation based on salient transition region. Pattern Recognit 52:317–331CrossRef
28.
Feng C, Zhao D, Huang M (2016) Segmentation of longitudinal brain MR images using bias correction embedded fuzzy c-means with non-locally spatio-temporal regularization. J Vis Commun Image Represent 38:517–529CrossRef
29.
Xiao Y, Yu J (2008) Semi-supervised clustering based on affinity propagation algorithm. J Softw 19(11):2803–2813MATHCrossRef
30.
Wang K-J, Zhang J-Y, Li D, Zhang X-N, Guo T (2007) Adaptive affinity propagation clustering. Acta Autom Sin 33(12):1242–1246MATH
31.
Calinski T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat 3(1):1–27MathSciNetMATH
32.
Bhargavi MS, Gowda SD (2015) A novel validity index with dynamic cut-off for determining true clusters. Pattern Recognit 48(11):3673–3687CrossRef
33.
Strehl A (2002) Relationship-based clustering and cluster ensembles for high-dimensional data mining. University of Texas at Austin, Austin
34.
Wang K-J, Li J, Zhang J-Y, Guo L-X (2008) Experimental comparison of clusters number estimation for cluster analysis. Comput Eng 34(9):198–199
35.
Kapp AV, Tibshirani R (2007) Are clusters found in one dataset present in another dataset? Biostatistics 8(1):9–31MATHCrossRef
36.
Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20(1):53–65MATHCrossRef
37.
Pal NR, Bezdek JC (1995) On cluster validity for the fuzzy c-means model. IEEE Trans Fuzzy Syst 3(3):370–379CrossRef
38.
Bezdek JC, Pal NR (1998) Some new indexes of cluster validity. IEEE Trans Syst Man Cybern-Part B Cybern 28(3):301–315CrossRef
39.
Shieh H (2014) Robust validity index for a modified subtractive clustering algorithm. Appl Soft Comput 22:47–59CrossRef
40.
Iam-On N, Boongoen T, Garrett S, Price C (2011) A link-based approach to the cluster ensemble problem. IEEE Trans Pattern Anal Mach Intell 33(12):2396–2409CrossRef
Metadaten
Titel
Automatic grayscale image segmentation based on Affinity Propagation clustering
verfasst von
Shibing Zhou
Zhenyuan Xu
Publikationsdatum
15.02.2019
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications / Ausgabe 1/2020
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-019-00785-4

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