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Analysis and Design of Image Segmentation Algorithm Based on Super-pixel and Graph Cut

International Journal of Advanced Network, Monitoring and Controls
Volume 3 (2018): Issue 4 (January 2018)
By:
Feng Xiao and  Hao Sun  
Open Access
|Oct 2019

Figures & Tables

Figure 1.

Image (a) segmentation result of different superpixels with the 32compact factor
Image (a) segmentation result of different superpixels with the 32compact factor

Figure 2.

Image (a) different compact coefficient segmentation results when the number of superpixels is 64
Image (a) different compact coefficient segmentation results when the number of superpixels is 64

Figure 3.

Network of terminal vertices and ordinary vertices
Network of terminal vertices and ordinary vertices

Figure 4.

Maximum flow minimum cut diagram
Maximum flow minimum cut diagram

Figure 5.

Algorithm flowchart
Algorithm flowchart

Figure 6.

Relationship between recall rate and accuracy
Relationship between recall rate and accuracy

Figure 7.

Relationship between K value and recall rate
Relationship between K value and recall rate

Figure 8.

Relationship between K value and accuracy diagram
Relationship between K value and accuracy diagram

Figure 9.

Relationship between the number of super pixels and the accuracy rate
Relationship between the number of super pixels and the accuracy rate
DOI: https://doi.org/10.21307/ijanmc-2019-017 | Journal eISSN: 2470-8038
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Language: English
Page range: 25 - 30
Published on: Oct 14, 2019
Published by: Xi’an Technological University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Interactive Segmentation,
Super-Pixel,
Graph Cut,
Maximum Flow/ Minimum Cut,
K-Means,
SLIC
Related subjects:
Computer sciences,
Computer sciences, other

© 2019 Feng Xiao, Hao Sun, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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