Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Image Semantic Segmentation Using Improved ENet Network

  • Received : 2020.11.19
  • Accepted : 2021.05.23
  • Published : 2021.10.31
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Abstract

An image semantic segmentation model is proposed based on improved ENet network in order to achieve the low accuracy of image semantic segmentation in complex environment. Firstly, this paper performs pruning and convolution optimization operations on the ENet network. That is, the network structure is reasonably adjusted for better results in image segmentation by reducing the convolution operation in the decoder and proposing the bottleneck convolution structure. Squeeze-and-excitation (SE) module is then integrated into the optimized ENet network. Small-scale targets see improvement in segmentation accuracy via automatic learning of the importance of each feature channel. Finally, the experiment was verified on the public dataset. This method outperforms the existing comparison methods in mean pixel accuracy (MPA) and mean intersection over union (MIOU) values. And in a short running time, the accuracy of the segmentation and the efficiency of the operation are guaranteed.

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References

  1. R. Moreno, M. Grana, D. M. Ramik, and K. Madani, "Image segmentation on spherical coordinate representation of RGB colour space," IET Image Processing, vol. 6, no. 9, pp. 1275-1283, 2012. https://doi.org/10.1049/iet-ipr.2011.0634
  2. Z. C. Jing, J. Ye, and G. L. Xu, "A geometric flow approach for region-based image segmentation-theoretical analysis," Acta Mathematicae Applicatae Sinica, English Series, vol. 34, no. 1, pp. 65-76, 2018. https://doi.org/10.1007/s10255-018-0723-4
  3. L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, "DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFS," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 4, pp. 834-848, 2017. https://doi.org/10.1109/TPAMI.2017.2699184
  4. T. Ling and W. Wu, "An image segmentation algorithm using visual saliency and graph cut," Paper Asia, vol. 2, no. 1, pp. 114-118, 2019.
  5. A. V. Anjikar, K. Ramteke, and S. Chauvan, "Color image segmentation using region growth and merge improved technique," International Journal of Computer Sciences and Engineering, vol. 7, no. 3, pp. 1070-1072, 2019.
  6. D. Stosic, D. Stosic, T. B. Ludermir, and T. I. Ren, "Natural image segmentation with non-extensive mixture models," Journal of Visual Communication and Image Representation, vol. 63, article no. 102598, 2019. https://doi.org/10.1016/j.jvcir.2019.102598
  7. U. Anitha, S. Malarkkan, G. A. Jebaselvi, and R. Narmadha, "Sonar image segmentation and quality assessment using prominent image processing techniques," Applied Acoustics, vol. 148, pp. 300-307, 2019. https://doi.org/10.1016/j.apacoust.2018.12.038
  8. M. Aamir, Y. F. Pu, W. A. Abro, H. Naeem, and Z. Rahman, "A hybrid approach for object proposal generation," in The Proceedings of the International Conference on Sensing and Imaging. Cham, Switzerland: Springer, 2017, pp. 251-259.
  9. Y. Wang, Q. Qi, Y. Liu, L. Jiang, and J. Wang, "Unsupervised segmentation parameter selection using the local spatial statistics for remote sensing image segmentation," International Journal of Applied Earth Observation and Geoinformation, vol. 81, pp. 98-109, 2019. https://doi.org/10.1016/j.jag.2019.05.004
  10. X. Wang, W. Li, C. Zhang, W. Lou, and R. Song, "An adaptable active contour model for medical image segmentation based on region and edge information," Multimedia Tools and Applications, vol. 78, no. 23, pp. 33921-33937, 2019. https://doi.org/10.1007/s11042-019-08073-3
  11. T. Arora and R. Dhir, "A variable region scalable fitting energy approach for human Metaspread chromosome image segmentation," Multimedia Tools and Applications, vol. 78, no. 7, pp. 9383-9404, 2019. https://doi.org/10.1007/s11042-018-6550-z
  12. G. Qin and Q. Li, "Pavement image segmentation based on fast FCM clustering with spatial information in Internet of Things," Multimedia Tools and Applications, vol. 78, no. 5, pp. 5181-5191, 2019. https://doi.org/10.1007/s11042-017-4683-0
  13. Y. Yuan and Y. C. Lo, "Improving dermoscopic image segmentation with enhanced convolutional-deconvolutional networks," IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 2, pp. 519-526, 2017. https://doi.org/10.1109/jbhi.2017.2787487
  14. D. Guo, Y. Pei, K. Zheng, H. Yu, Y. Lu, and S. Wang, "Degraded image semantic segmentation with densegram networks," IEEE Transactions on Image Processing, vol. 29, pp. 782-795, 2019. https://doi.org/10.1109/tip.2019.2936111
  15. T. Manabe, K. Tomonaga, K. Fujita, Y. Shibata, T. Kosaka, and T. Adachi, "CNN architecture for surgical image segmentation with recursive structure and flip-based upsampling," International Journal of Networking and Computing, vol. 10, no. 2, pp. 259-276, 2020. https://doi.org/10.15803/ijnc.10.2_259
  16. R. Ratnakumar and S. J. Nanda, "A low complexity hardware architecture of K-means algorithm for real-time satellite image segmentation," Multimedia Tools and Applications, vol. 78, no. 9, pp. 11949-11981, 2019. https://doi.org/10.1007/s11042-018-6726-6
  17. A. W. Rosyadi and N. Suciati, "Image segmentation using transition region and k-means clustering," IAENG International Journal of Computer Science, vol. 47, no. 1, pp. 47-55, 2020.
  18. Y. Zheng, X. Zhang, F. Wang, T. Cao, M. Sun, and X. Wang, "Detection of people with camouflage pattern via dense deconvolution network," IEEE Signal Processing Letters, vol. 26, no. 1, pp. 29-33, 2019. https://doi.org/10.1109/LSP.2018.2825959
  19. R. Jin and G. Weng, "Active contour model based on fuzzy c-means for image segmentation," Electronics Letters, vol. 55, no. 2, pp. 84-86, 2019. https://doi.org/10.1049/el.2018.5307
  20. M. Aamir, Y. F. Pu, Z. Rahman, W. A. Abro, H. Naeem, F. Ullah, and A. M. Badr, "A hybrid proposed framework for object detection and classification," Journal of Information Processing Systems, vol. 14, no. 5, pp. 1176-1194, 2018. https://doi.org/10.3745/JIPS.02.0095
  21. S. Ozturk and B. Akdemir, "Cell-type based semantic segmentation of histopathological images using deep convolutional neural networks," International Journal of Imaging Systems and Technology, vol. 29, no. 3, pp. 234-246, 2019. https://doi.org/10.1002/ima.22309
  22. Y. Wu and S. Misra, "Intelligent image segmentation for organic-rich shales using random forest, wavelet transform, and hessian matrix," IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 7, pp. 1144-1147, 2020. https://doi.org/10.1109/lgrs.2019.2943849
  23. Y. Lu, Y. Chen, D. Zhao, and J. Chen, "Graph-FCN for image semantic segmentation," in Advances in Neural Networks - ISSN 2019. Cham, Switzerland: Springer, 2019, pp. 97-105.
  24. H. Zhou, A. Han, H. Yang, and J. Zhang, "Edge gradient feature and long distance dependency for image semantic segmentation," IET Computer Vision, vol. 13, no. 1, pp. 53-60, 2019. https://doi.org/10.1049/iet-cvi.2018.5035
  25. A. Abu and R. Diamant, "Enhanced fuzzy-based local information algorithm for sonar image segmentation," IEEE Transactions on Image Processing, vol. 29, pp. 445-460, 2019. https://doi.org/10.1109/tip.2019.2930148
  26. Z. Huang, G. Huang, and L. Cheng, "Medical image segmentation of blood vessels based on Clifford algebra and Voronoi diagram," Journal of Software, vol. 13, no. 6, pp. 360-373, 2018. https://doi.org/10.17706/jsw.13.6.360-373
  27. H. Fakhi, O. Bouattane, M. Youssfi, and H. Ouajji, "Distributed GPU-based k-means algorithm for data-intensive applications: large-sized image segmentation case," International Journal of Advanced Computer Science and Applications, vol. 8, no. 12, pp. 171-178, 2017.
  28. A. R. Subhamathi, "Ultrasound image segmentation based on information diffusion model," International Journal of Computer Sciences and Engineering, vol. 6, no. 3, pp. 205-210, 2018. https://doi.org/10.26438/ijcse/v6si3.205210
  29. T. C. Zhang, J. Zhang, J. P. Zhang, M. L. Smith, and E. R. Hancock, "A novel model and method based on Nash equilibrium for medical image segmentation," Journal of Medical Imaging and Health Informatics, vol. 8, no. 5, pp. 872-880, 2018. https://doi.org/10.1166/jmihi.2018.2387
  30. S. Ren and F. Liu, "The optimal thresholding technique for image segmentation using fuzzy Otsu method," Advances in Computational Sciences and Technology, vol. 11, no. 6, pp. 445-454, 2018.
  31. A. K. M. Khairuzzaman and S. Chaudhury, "Masi entropy based multilevel thresholding for image segmentation," Multimedia Tools and Applications, vol. 78, no. 23, pp. 33573-33591, 2019. https://doi.org/10.1007/s11042-019-08117-8