Standard Deviation Clustering Combined with Visual Psychological Test Algorithm for Image Segmentation

  • Zhenggang Wang
  • Jin JinEmail author
  • Zhong Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11888)


Detection of the visual salient image area for image segmentation, image recognition, and adaptive compression application is beneficial. It makes an object, a person, or some pixels stand out against the background of the image and provide support for image recognition and target detection. The detection can simplify the process of computer visual image processing and improve the effect and efficiency of computer visual inspection. This paper introduces a kind of salient detection method, without any manual intervention, and uses the method of decomposing brightness, color space, negative map solution, and standard deviation to find the super-distance pixel in the image. The method of clustering is used to separate the region of objects and image background, and output RGB color salient objects image. Moreover, it can accurately highlight the object contour and internal pixels. This method studies the characteristics of the original pixels such as brightness or color and utilizes the image basis features to achieve the image saliency detection. It has high adaptive detection ability, low time complexity and high computational efficiency.


Saliency map Standard deviation Negative map Clustering Salient color objects 


  1. 1.
    Zhai, Y., Shah, M.: Visual attention detection in video sequences using spatiotemporal cues. In: Proceedings of the 14th ACM International Conference on Multimedia, pp. 815–824. ACM, October 2006Google Scholar
  2. 2.
    Achanta, R., Sustrunk, S.: Saliency detection using maximum symmetric surround. In: Proceedings of the IEEE Conference on Image Processing, Hong Kong, China, December 2010, pp. 2653–2656. IEEE, Hong Kong, December 2010Google Scholar
  3. 3.
    Cheng, M.-M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.-M.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 569–582 (2015)CrossRefGoogle Scholar
  4. 4.
    Achanta, R., Estrada, F., Wils, P., Süsstrunk, S.: Salient region detection and segmentation. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 66–75. Springer, Heidelberg (2008). Scholar
  5. 5.
    Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-tuned salient region detection. In: Proceedings of the IEEE CVPR, pp. 1597–1604. IEEE, Miami, June 2009Google Scholar
  6. 6.
    Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: Proceedings of the IEEE CVPR, pp. 1–8. IEEE, Minneapolis, June 2007Google Scholar
  7. 7.
    Vikram, T.N., Tscherepanow, M., Wrede, B.: A saliency map based on sampling an image into random rectangular regions of interest. Pattern Recogn. 45(9), 3114–3124 (2012)CrossRefGoogle Scholar
  8. 8.
    Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915–1926 (2012)CrossRefGoogle Scholar
  9. 9.
    Otazu, X., Parraga, C.A., Vanrell, M.: Toward a unified chromatic induction model. J. Vis. 10(12), 5 (2010)CrossRefGoogle Scholar
  10. 10.
    Murray, N., Vanrell, M., Otazu, X., Parraga, C.A.: Saliency estimation using a non-parametric low-level vision model. In: Proceedings of the IEEE CVPR, pp. 433–440. IEEE, Colorado Springs, August 2011Google Scholar
  11. 11.
    Zhang, L., Tong, M.H., Marks, T.K., Shan, H., Cottrell, G.W.: SUN: a Bayesian framework for saliency using natural statistics. J. Vis. 8(7), 32–32 (2008)CrossRefGoogle Scholar
  12. 12.
    Zhang, L., Tong, M.H., Cottrell, G.W.: SUNDAy: saliency using natural statistics for dynamic analysis of scenes. In: Proceedings of the 31st Annual Cognitive Science Conference, pp. 2944–2949. AAAI Press, Cambridge, December 2009Google Scholar
  13. 13.
    Kanan, C., Tong, M.H., Zhang, L., Cottrell, G.W.: SUN: top-down saliency using natural statistics. Vis. Cogn. 17(6–7), 979–1003 (2009)CrossRefGoogle Scholar
  14. 14.
    Rahtu, E., Kannala, J., Salo, M., Heikkilä, J.: Segmenting salient objects from images and videos. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6315, pp. 366–379. Springer, Heidelberg (2010). Scholar
  15. 15.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE CVPR, pp. 770–778, June 2016Google Scholar
  16. 16.
    Kar, A., Tulsiani, S., Carreira, J., Malik, J.: Category-specific object reconstruction from a single image. In: Proceedings of the IEEE CVPR, pp. 1966–1974. IEEE, June 2015Google Scholar
  17. 17.
    Cheng, Y., Fu, H., Wei, X., Xiao, J., Cao, X.: Depth enhanced saliency detection method. In: Proceedings of International Conference on Internet Multimedia Computing and Service, p. 23. ACM, July 2014Google Scholar
  18. 18.
    Wang, A., Wang, M., Li, X., Mi, Z., Zhou, H.: A two-stage bayesian integration framework for salient object detection on light field. Neural Process. Lett. 46(3), 1083–1094 (2017)CrossRefGoogle Scholar
  19. 19.
    Wang, A., Wang, M.: RGB-D salient object detection via minimum barrier distance transform and saliency fusion. IEEE Sig. Process. Lett. 24(5), 663–667 (2017)CrossRefGoogle Scholar
  20. 20.
    Li, N., Ye, J., Ji, Y., Ling, H., Yu, J.: Saliency detection on light field. In: Proceedings of the IEEE CVPR, pp. 2806–2813. IEEE, June 2014Google Scholar
  21. 21.
    He, S., Lau, R.W., Liu, W., Huang, Z., Yang, Q.: SuperCNN: a superpixelwise convolutional neural network for salient object detection. Int. J. Comput. Vis. 115(3), 330–344 (2015)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Cebeci, Z., Yildiz, F.: Comparison of K-means and fuzzy C-means algorithms on different cluster structures. JAI 6(3), 13–23 (2015)Google Scholar
  23. 23.
    Dhanachandra, N., Manglem, K., Chanu, Y.J.: Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Proc. Comput. Sci. 54, 764–771 (2015)CrossRefGoogle Scholar
  24. 24.
    Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)CrossRefGoogle Scholar
  25. 25.
    W-l, I.: Getting the brain’s attention. Science 278, 35–37 (1997)CrossRefGoogle Scholar
  26. 26.
    Kozasa, E.H., et al.: Meditation training increases brain efficiency in an attention task. Neuroimage 59(1), 745–749 (2012)CrossRefGoogle Scholar
  27. 27.
    Gopalakrishnan, V., Hu, Y., Rajan, D.: Random walks on graphs for salient object detection in images. IEEE Trans. Image Process. 19(12), 3232–3242 (2010)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Computer ApplicationChinese Academy of SciencesChengduChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Chengdu Customs District of People’s Republic of ChinaChengduChina
  4. 4.Leshan Vocational and Technical CollegeLeshanChina

Personalised recommendations