Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (2): 96-101.doi: 10.6040/j.issn.1672-3961.0.2018.242

• Machine Learning & Data Mining • Previous Articles     Next Articles

An unsupervised color image segmentation method based on fusion of multiple methods

Xinyu DONG1,2,3(),Hanyue CHEN1,2,*(),Jiaguo LI3,Qingyan MENG3,Shihe XING1,2,Liming ZHANG1,2   

  1. 1. College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, Fujian, China
    2. University Key Lab of Soil Ecosystem Health and Regulation in Fujian, Fuzhou 350002, Fujian, China
    3. The Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, Beijing, China
  • Received:2018-06-07 Online:2019-04-20 Published:2019-04-19
  • Contact: Hanyue CHEN E-mail:dsammy@126.com;chenhanyue.420@163.com
  • Supported by:
    海南自然科学基金创新研究团队资助项目(2017CXTD015);高分辨率对地观测系统重大专项资助项目(30-Y20A07-9003-17/18);国家自然科学基金资助项目(41401399)

Abstract:

An unsupervised color image segmentation method based on fusion of multiple methods was proposed, which considered the defects of traditional K-means clustering color image segmentation method, such as the need to set the number of initial segmentation categories artificially and the vulnerability to noise interference, etc. First of all, the original image was processed by spectral information enhancement to improving the efficiency of image information extraction. Next, the number of K-means clustering segmentation categories was determined automatically by using Davies-Bouldin Index, and the clustering analysis was carried out for images and each pixel in an image was labeled. Then, the labeled image was segmented by combining the Gauss-Markov random field theory. Finally, the image after-processing was made based on the morphological operators. The segmentation experiments were carried out by using different methods, the results showed that the segmentation effect of the proposed method was closer to the origin image, and the proposed method had good robustness. And the results of quantitative evaluation of segmentation showed that this method had more advantages in segmentation precision and accuracy.

Key words: color image segmentation, decorrelation stretch, K-means clustering, Gauss-Markov random field, morphological operators

CLC Number: 

  • TP751

Fig.1

The original image"

Fig.2

The processed results of decorrelation stretch"

Table 1

The correlation statistics before and afterdecorrelation stretch"

阶段 波段 Band1 Band2 Band3
Band1 1.000 0 0.976 1 0.772 0
处理前 Band2 0.976 1 1.000 0 0.884 5
Band3 0.772 0 0.884 5 1.000 0
Band1 1.000 0 0.308 7 0.002 9
处理后 Band2 0.308 7 1.000 0 0.039 3
Band3 0.002 9 0.039 3 1.000 0

Fig.3

Comparison of segmentation results of 12003"

Fig.4

Comparison of segmentation results of 124084"

Fig.5

Comparison of segmentation results of 24036"

Table 2

Comparison of Dice coefficient"

编号 本研究算法 K-means FCM FRFCM
12003 0.968 8 0.880 3 0.796 6 0.787 6
124084 0.960 8 0.831 4 0.434 7 0.909 6
24036 0.982 6 0.978 6 0.319 3 0.979 6

Table 3

Comparison of Jaccard similarity coefficient"

编号 本研究算法 K-means FCM FRFCM
12003 0.939 4 0.786 1 0.662 0 0.649 6
124084 0.934 1 0.711 5 0.277 7 0.834 1
24036 0.965 8 0.958 1 0.190 0 0.959 9
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