Adaptive Ensemble Clustering for Image Segmentation in Remote Sensing

  • Tingting YaoEmail author
  • Chang Liu
  • Zhian Deng
  • Xiaoming Liu
  • Jiacheng Liu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 463)


Image segmentation is a fundamental computer vision task. Although many approaches have been proposed, obtaining accurate results in some special applications are still not easy. In this paper, we propose a novel image segmentation method for remote sensing based on adaptive cluster ensemble learning. The clustering parameter of each image is calculated with affinity propagation automatically. Then, multiple clusterers are trained separately and the predictions of them are combined under the ensemble learning framework. In this way, the robustness of each clusterer could be enhanced. Experimental results demonstrate the effectiveness of our proposed method.


Ensemble learning Affinity propagation Remote sensing Image segmentation 



This work was supported by the National Natural Science Foundation of China (61301132), the Natural Science Foundation of Liaoning Province (201601065), the National Key Technology R&D Program (2015BAG20B02) and the Fundamental Research Funds for the Central Universities (3132017129, 32016347).


  1. 1.
    Gao, G., Wen, C., Wang, H.: Fast and robust image segmentation with active contours and student’s-t mixture model. Patt. Recogn. 63, 71–86 (2017)Google Scholar
  2. 2.
    Niu, S., Chen, Q., Sisternes, L.D., Ji, Z., Zhou, Z., Rubin, D.L.: Robust noise region-based active contour model via local similarity factor for image segmentation. Patt. Recogn. 61, 104–119 (2017)Google Scholar
  3. 3.
    Rajaby, E., Ahadi, S.M., Aghaeinia, H.: Robust color image segmentation using fuzzy c-means with weighted hue and intensity. Digit. Sig. Process. 51, 170–183 (2016)Google Scholar
  4. 4.
    Wang, S., Yin, Y., Cao, G., Wei, B., Zheng, Y., Yang, G.: Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing 149(PB), 708–717 (2015)Google Scholar
  5. 5.
    Tekin, C., Yoon, J., Schaar, M.V.D.: Adaptive ensemble learning with confidence bounds. IEEE Trans. Sig. Process. PP(99), 1 (2016)Google Scholar
  6. 6.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)Google Scholar
  7. 7.
    Li, Z., Chen, J.: Superpixel segmentation using linear spectral clustering. In: Computer Vision and Pattern Recognition, pp. 1356–1363. IEEE (2015)Google Scholar
  8. 8.
    Schapire, R.E.: The strength of weak learnability. In: 1989 Symposium on Foundations of Computer Science, vol. 5, pp. 28–33. IEEE (1989)Google Scholar
  9. 9.
    Wang, Q.: Research on some key issues of ensemble learning (Doctoral Dissertation, Fudan University) (2011)Google Scholar
  10. 10.
    Mori, G.: Guiding model search using segmentation. In: Proceedings, vol. 2, pp. 1417–1423 (2005)Google Scholar
  11. 11.
    Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)Google Scholar
  12. 12.
    Zhou, Z.H., Tang, W.: Clusterer ensemble. Knowl.-Based Syst. 19(1), 77–83 (2006)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Tingting Yao
    • 1
    Email author
  • Chang Liu
    • 1
  • Zhian Deng
    • 1
  • Xiaoming Liu
    • 1
  • Jiacheng Liu
    • 2
  1. 1.Dalian Maritime UniversityDalianChina
  2. 2.Northeastern UniversityBostonUSA

Personalised recommendations