Hongyu Lu This email address is being protected from spambots. You need JavaScript enabled to view it.

1Henan Institute of Economics and Trade Zhengzhou 450000, China


 

Received: September 7, 2021
Accepted: October 6, 2021
Publication Date: November 1, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202208_25(4).0005  


ABSTRACT


Image segmentation is the pre-processing stage of image analysis. It divides the image into different re-gions for the subsequent image analysis. The traditional segmentation algorithms can not deal with the images with complex background and uneven gray level effectively. Especially, for the art images seg-mentation with high resolution, the distinguishment degree between foreground of the object to be seg-mented and the background is small, which can lead to the incomplete segmentation effect. Therefore, this paper proposes a graph theorybased Fast Linear Iterative Clustering (FLIC) multi-feature fusion model for art image segmentation. First, the FLIC segmentation algorithm is used to pre-segment the su-perpixel of raw image. Second, the HOG feature, Lab color feature and spatial location feature are ex-tracted. Third, A multi-feature fusion strategy based on superpixel is designed. Four, the fast image seg-mentation based on multi-feature fusion is realized by using the graph theory. The graph theory is opti-mized by time domain convolution. Finally, the comparison experiments with other state-of-the-art methods are conducted on the public datasets: Berkeley Segmentation Dataset and Culture Gene Online . The results show that the proposed algorithm has better effect in terms of evaluation indexes (USE, BR, ASA and Time).


Keywords: art image segmentation, graph theory, FLIC, multi-feature fusion, time domain convolution


REFERENCES


  1. [1] S. Yin and H. Li, (2020) “Hot Region Selection Based on Selective Search and Modified Fuzzy C-Means in Remote Sensing Images" IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13: 5862–5871. DOI: 10.1109/JSTARS.2020.3025582.
  2. [2] P. Felzenszwalb and D. Huttenlocher, (2004) “Efficient graph-based image segmentation" International Journal of Computer Vision 59(2): 167–181. DOI: 10.1023/B:VISI.0000022288.19776.77.
  3. [3] R. Achanta and S. Süsstrunk. “Superpixels and polygons using simple non-iterative clustering”. In: 2017- January. 2017, 4895–4904. DOI: 10.1109/CVPR.2017.520.
  4. [4] T. Cour, F. Bénézit, and J. Shi. “Spectral segmentation with multiscale graph decomposition”. In: II. 2005, 1124–1131. DOI: 10.1109/CVPR.2005.332.
  5. [5] Z. Li, X.-M. Wu, and S.-F. Chang. “Segmentation using superpixels: A bipartite graph partitioning approach”. In: 2012, 789–796. DOI: 10.1109/CVPR.2012.6247750.
  6. [6] P. Arbeláez, M. Maire, C. Fowlkes, and J. Malik, (2011) “Contour detection and hierarchical image segmentation" IEEE Transactions on Pattern Analysis and Machine Intelligence 33(5): 898–916. DOI: 10.1109/TPAMI.2010.161.
  7. [7] J. Li,W. Tang, J.Wang, and X. Zhang, (2019) “A multilevel color image thresholding scheme based on minimum cross entropy and alternating direction method of multipliers" Optik 183: 30–37. DOI: 10.1016/j.ijleo.2019.02.004.
  8. [8] J. Yang, Z. Kang, S. Cheng, Z. Yang, and P. H. Akwensi, (2020) “An individual tree segmentation method based on watershed algorithm and three-dimensional spatial distribution analysis from airborne LiDAR point clouds" IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13: 1055–1067.
  9. [9] S. Yin, H. Li, D. Liu, and S. Karim, (2020) “Active contour modal based on density-oriented BIRCH clustering method for medical image segmentation" Multimedia Tools and Applications 79(41-42): 31049–31068. DOI:10.1007/s11042-020-09640-9.
  10. [10] Z. Ye, R. Yi, M. Yu, Y.-J. Liu, and Y. He. “Fast computation of content-sensitive superpixels and supervoxels using Q-distances”. In: 2019-October. 2019, 3769–3778. DOI: 10.1109/ICCV.2019.00387.
  11. [11] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, (2012) “SLIC superpixels compared to state of-the-art superpixel methods" IEEE Transactions on Pattern Analysis and Machine Intelligence 34(11):2274–2281. DOI: 10.1109/TPAMI.2012.120.
  12. [12] J. Zhao, R. Bo, Q. Hou, M.-M. Cheng, and P. Rosin, (2018) “FLIC: Fast linear iterative clustering with active search" Computational Visual Media 4(4): 333–348. DOI: 10.1007/s41095-018-0123-y.
  13. [13] Z. Zhang, F. Xing, H. Wang, Y. Yan, Y. Huang, X. Shi, and L. Yang, (2018) “Revisiting graph construction for fast image segmentation" Pattern Recognition 78: 344–357. DOI: 10.1016/j.patcog.2018.01.037.
  14. [14] M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa. “Entropy rate superpixel segmentation”. In: 2011, 2097–2104. DOI: 10.1109/CVPR.2011.5995323.
  15. [15] S. Yin, H. Li, and L. Teng, (2020) “Airport Detection Based on Improved Faster RCNN in Large Scale Remote Sensing Images" Sensing and Imaging 21(1): DOI: 10.1007/s11220-020-00314-2.
  16. [16] M. Cordts, T. Rehfeld, M. Enzweiler, U. Franke, and S. Roth, (2017) “Tree-structured models for efficient multi-cue scene labeling" IEEE Transactions on Pattern Analysis and Machine Intelligence 39(7): 1444–1454. DOI: 10.1109/TPAMI.2016.2592911.


    
 

0.7
2020CiteScore
 
 
33rd percentile
Powered by  Scopus

SCImago Journal & Country Rank

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.