Advertisement

Extraction of Salient Region Based on Visual Perception

  • Yongchang Li
  • Pengluo LuEmail author
  • Cheng Cheng
  • Jianing Hao
  • Li Liu
  • Jun Zhu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 550)

Abstract

In order to better analysis and understand digital images, a salient region extraction algorithm based on visual perception is proposed. First, a multi-scale difference of gaussian filter is used to the image, which simulates the center-peripheral response of the human visual nerve cell; Then, we use the pulse cosine transform to extract the edge information of the image, simulate the side inhibition process of the nerve cell, and obtain the image feature maps at different scales. Finally, the threshold segmentation and regional expansion of the feature graph are used to construct the focal window in the region with the most prominent image features. The experiment was performed on 500 images of the salient object database provided by Microsoft Research Asia (MSRA) using the proposed method, take \( \beta^{2} = 0.3 \), the value of F-measure is as high as 0.816. Results show that the method can be effectively to extract salient area of the image with different content, different target location and size, and has the location and size on the adaptability.

Keywords

Space technology Data analyzing Visual perception Salient region Pulse cosine transform Feature extraction 

Notes

Acknowledgment

This work was supported by the Youth Foundation of High-resolution Program (No. GFZX04061502).

References

  1. 1.
    Marr, D.: Visual information processing: the structure and creation of visual representations. Philos. Trans. R. Soc. Lond. 290(1038), 199–218 (1980)CrossRefGoogle Scholar
  2. 2.
    Treisman, A.M., Gelade, G.: A feature integration theory of attention. Cogn. Psychol. 12(1), 97–136 (1980)CrossRefGoogle Scholar
  3. 3.
    Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Hum. Neurobiol. 4(4), 219–227 (1985)Google Scholar
  4. 4.
    Itti, L., Koch, C., Niebur. E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Comput. Soc. (1998)Google Scholar
  5. 5.
    Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR 2007, pp. 1–8. IEEE (2007)Google Scholar
  6. 6.
    Guo, C., Zhang, L.: A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans. Image Process. 19(1), 185–198 (2009)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Achanta, R., Hemami, S., Estrada, F., et al.: Frequency-tuned salient region detection. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, pp. 1597–1604. IEEE (2009)Google Scholar
  8. 8.
    Yu, Y., Wang, B., Zhang, L.: Pulse discrete cosine transform for saliency-based visual attention. In: International Conference on Development and Learning, pp. 1–6. IEEE (2009) Google Scholar
  9. 9.
    Zhang, X.: Computational models and applications of the retinal color vision. University of Electronic Science and technology of China. (2017)Google Scholar
  10. 10.
    Li, S.: Research on visual perception based spatial gamut mapping. Tianjin University (2016)Google Scholar
  11. 11.
    Xiong, W., Xu, Y., Cui, Y., et al.: Geometric feature extraction of ship in high-resolution synthetic aperture radar images. Acta Photonica Sinica 47(1), 49–58 (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yongchang Li
    • 1
  • Pengluo Lu
    • 2
    Email author
  • Cheng Cheng
    • 1
  • Jianing Hao
    • 1
  • Li Liu
    • 1
  • Jun Zhu
    • 1
  1. 1.DFH Satellite Co., Ltd.BeijingChina
  2. 2.Institute of Geology and GeophysicsChinese Academy of SciencesBeijingChina

Personalised recommendations