Soft spectral clustering ensemble applied to image segmentation

doi:10.1007/s11704-010-0161-9

Front Comput Sci Chin

0, Vol.

Issue () : 66-78 https://doi.org/10.1007/s11704-010-0161-9

Soft spectral clustering ensemble applied to image segmentation

Jianhua JIA^1,2(

), Bingxiang LIU¹, Licheng JIAO²

1. School of Information Engineering, Jingdezhen Ceramic Institute, Jingdezhen 333002, China; 2. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China and Institute of Intelligent Information Processing, Xidian University, Xi'an 710071, China

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Abstract

An unsupervised learning algorithm, named soft spectral clustering ensemble (SSCE), is proposed in this paper. Until now many proposed ensemble algorithms cannot be used on image data, even images of a mere 256 × 256 pixels are too expensive in computational cost and storage. The proposed method is suitable for performing image segmentation and can, to some degree, solve some open problems of spectral clustering (SC). In this paper, a random scaling parameter and Nystr?m approximation are applied to generate the individual spectral clusters for ensemble learning. We slightly modify the standard SC algorithm to aquire a soft partition and then map it via a centralized logcontrast transform to relax the constraint of probability data, the sum of which is one. All mapped data are concatenated to form the new features for each instance. Principal component analysis (PCA) is used to reduce the dimension of the new features. The final aggregated result can be achieved by clustering dimension-reduced data. Experimental results, on UCI data and different image types, show that the proposed algorithm is more efficient compared with some existing consensus functions.

Keywords spectral clustering (SC) Nystr?m approximation centralized logcontrast transform principal component analysis (PCA) ensemble learning

Corresponding Author(s): JIA Jianhua,Email:jjh163yx@163.com

Issue Date: 05 March 2011

Cite this article:

Jianhua JIA,Bingxiang LIU,Licheng JIAO. Soft spectral clustering ensemble applied to image segmentation[J]. Front Comput Sci Chin, 0, (): 66-78.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-010-0161-9
https://academic.hep.com.cn/fcs/EN/Y0/V/I/66

Fig.1 Framework of spectral clustering ensemble

Tab.1 Properties of Selected UCI Data Sets

Fig.2 Error rate of data sets of UCI data sets using different clustering algorithm Chess (ks-vs-kp). (a) Iris data set, (b) Sonar data set, (c) Wine data set, (d) Breast_cancer data set, (e) Chess (ks-vs-kp)

Fig.3 Error rate of large sample data set (Chess (ks-vs-kp) Data sets) of UCI data sets. (a) Average value and standard deviation of error rates by SC_Nys with the changing of the scaling parameters over Chess (ks-vs-kp) data set; (b) average value of SC_Nys and SSCE with different intervals of scaling parameter. The dashed line denotes the standard deviation of error rate by SSCE

Tab.2 Comparison of average error rate

Tab.3 CPU time of single runs of different consensus functions on the UCI data sets (s)

Fig.4 Performance of SSCE while varying the number of constituent clusterings. (a) Iris data; (b) Chess (ks-vs-kp) data

Fig.5 Results of 4-class synthesis texture image segmentation. (a) Original image; (b) real segmentation; (c) result of SC_Nys (92.32%); (d )result of MSCE (94.99%); (e) result of SSCE (97.10%)

Fig.6 Results of some synthesis texture images segmentation. (a) Original images; (b) results of SC_Nys; (c) results of MSCE; (d) results of SSCE

Fig.7 Results of Ku-band SAR image segmentation. (a) Original image; (b) result of SC_Nys; (c) result of MSCE; (d) result of SSCE

Fig.8 Results of X-band SAR image segmentation. (a) Original image; (b) result of SC_Nys; (c) result of MSCE; (d) result of the proposed algorithm

Fig.9 (a) SAR image-A (mountain/plain); (b )result of SC_Nys; (c) result of MSCE; (d) result of SSCE; (e) SAR image-B (arass/forest); (f) result of SC_Nys; (g) result of MSCE; (h) result of SSCE

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