Computer Science and Information Systems 2022 Volume 19, Issue 3, Pages: 1409-1426
https://doi.org/10.2298/CSIS220321040T
Full text (
1414 KB)
Cited by
BiSeNet-oriented context attention model for image semantic segmentation
Teng Lin (College of Information and Communication Engineering, Harbin Engineering University Harbin, China), tenglinheu@.com
Qiao Yulong (College of Information and Communication Engineering, Harbin Engineering University Harbin, China), qiaoyulong@hrbeu.edu.cn
When the traditional semantic segmentation model is adopted, the different feature importance of feature maps is ignored in the feature extraction stage, which results in the detail loss, and affects the segmentation effect. In this paper, we propose a BiSeNet-oriented context attention model for image semantic segmentation. In the BiSeNet, the spatial path is utilized to extract more low-level features to solve the problem of information loss in deep network layers. Context attention mechanism is used to mine high-level implied semantic features of images. Meanwhile, the focus loss is used as the loss function to improve the final segmentation effect by reducing the internal weighting. Finally, we conduct experiments on open data sets, and the results show that pixel accuracy, average pixel accuracy, and average Intersection-over-Union are greatly improved compared with other state-of-theart semantic segmentation models. It effectively improves the accuracy of feature extraction, reduces the loss of feature details, and improves the final segmentation effect.
Keywords: image semantic segmentation, BiSeNet, context attention, focus loss
Show references
Zhang G, Zhao K, Hong Y, et al. ”SHA-MTL: soft and hard attention multi-task learning for automated breast cancer ultrasound image segmentation and classification,” International Journal of Computer Assisted Radiology and Surgery, vol. 16, pp. 1719-1725, (2021).
H. Zhang et al. ”Multiscale Visual-Attribute Co-Attention for Zero-Shot Image Recognition,” IEEE Transactions on Neural Networks and Learning Systems, (2021). doi: 10.1109/TNNLS.2021.3132366.
X. Lei and H. Ouyang. ”Kernel-Based Intuitionistic Fuzzy Clustering Image Segmentation Based on Grey Wolf Optimizer With Differential Mutation,” IEEE Access, vol. 9, pp. 85455- 85463, (2021).
Fan Wang, Chen Chen, Haitao Zhang and Youhua Ma. ”Short-term Load Forecasting Based On Variational Mode Decomposition And Chaotic GreyWolf Optimization Improved Random Forest Algorithm,” Journal of Applied Science and Engineering, Vol. 26, No. 1, pp. 69-78, (2020).
Fung D, Liu Q, Zammit J, et al. ”Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19,” Journal of Translational Medicine, vol. 19, no. 1, (2021).
Xian S, Cheng Y, Chen K. ”A novel weighted spatial T-spherical fuzzy C-means algorithms with bias correction for image segmentation,” International Journal of Intelligent Systems, vol. 37, no. 2, (2022)
Zhang L, X Hu, Zhou Y, et al. ”Memristive DeepLab: A hardware friendly deep CNN for semantic segmentation,” Neurocomputing, vol. 451, pp. 181-191 (2021).
H. -Y. Han, Y. -C. Chen, P. -Y. Hsiao and L. -C. Fu. ”Using Channel-Wise Attention for Deep CNN Based Real-Time Semantic Segmentation With Class-Aware Edge Information,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 2, pp. 1041-1051, (2021).
Jisi A and Shoulin Yin. ”A New Feature Fusion Network for Student Behavior Recognition in Education,” Journal of Applied Science and Engineering, vol. 24, no. 2, pp. 133-140. (2021)
Russo G. ”On Unsupervised Methods for Medical Image Segmentation: Investigating Classic Approaches in Breast Cancer DCE-MRI,” Applied Sciences, vol. 12, no. 1. (2022)
Gurita A, Mocanu I G. ”Image Segmentation Using Encoder-Decoder with Deformable Convolutions,” Sensors,, vol. 21, no. 5, pp. 1570. (2021)
C. Lyu, G. Hu and D. Wang. ”HRED-Net: High-Resolution Encoder-Decoder Network for Fine-Grained Image Segmentation,” IEEE Access, vol. 8, pp. 38210-38220, (2020)
Z. Tian, T. He, C. Shen and Y. Yan. ”Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3121-3130, (2019). doi: 10.1109/CVPR.2019.00324.
CaiW, Zhai B, Liu Y, et al. ”Quadratic Polynomial Guided Fuzzy C-means and Dual Attention Mechanism for Medical Image Segmentation,” Displays, vol. 70, no. 6, pp. 102106. (2021)
A. Bera, Z. Wharton, Y. Liu, N. Bessis and A. Behera. ”Attend and Guide (AG-Net): A Keypoints-Driven Attention-Based Deep Network for Image Recognition,” IEEE Transactions on Image Processing, vol. 30, pp. 3691-3704, (2021).
Yang T, Yoshimura Y, Morita A, et al. ”Pyramid Predictive Attention Network for Medical Image Segmentation,” IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, vol. E102, no. A(9), pp. 1225-1234. (2019)
Al-Huda Z, Zhai D, Yang Y, et al. ”Optimal Scale of Hierarchical Image Segmentation with Scribbles Guidance for Weakly Supervised Semantic Segmentation,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 35, no. 10, (2021)
Lsel P D, Kamp T, Jayme A, et al. ”Introducing Biomedisa as an open-source online platform for biomedical image segmentation,” Nature Communications, 11(5577). (2020)
Guo H, Yang D. ”PRDNet: Medical image segmentation based on parallel residual and dilated network,” Measurement,vol. 173, no. 1, pp. 108661. (2020)
Huang M, Huang S, Zhang Y, et al. ”Medical Image Segmentation Using Deep learning with Feature Enhancement,” IET Image Processing, vol. 14, no. 5. (2020)
Olimov B, Sanjar K, Din S, et al. ”FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers,” Multimedia Systems, vol. 27, no. 4, pp. 637-650, 2021.
Zheng T, Duan Z, Wang J, et al. ”Research on Distance Transform and Neural Network Lidar Information Sampling Classification-Based Semantic Segmentation of 2D Indoor Room Maps,” Sensors, vol. 21, no. 4, pp. 1365. (2021)
Shoulin Yin, Hang Li, Desheng Liu and Shahid Karim. ”Active Contour Modal Based on Density-oriented BIRCH Clustering Method for Medical Image Segmentation,” Multimedia Tools and Applications, Vol. 79, pp. 31049-31068, (2020).
Wech T, Ankenbrand M J, Bley T A, et al. ”A data-driven semantic segmentation model for direct cardiac functional analysis based on undersampled radial MR cine series,” Magnetic Resonance in Medicine, vol. 87. (2022)
Jiang, D., Li, H., Yin, S. ”Speech Emotion Recognition Method Based on Improved Long Short-term Memory Networks,” International Journal of Electronics and Information Engineering, Vol. 12, No. 4, pp. 147-154. (2020)
Jiang M, Zhai F, Kong J. ”Sparse Attention Module for optimizing semantic segmentation performance combined with a multi-task feature extraction network,” The Visual Computer, vol. 12. (2021)
R. Yi, Y. Huang, Q. Guan, M. Pu and R. Zhang. ”Learning From Pixel-Level Label Noise: A New Perspective for Semi-Supervised Semantic Segmentation,” IEEE Transactions on Image Processing, vol. 31, pp. 623-635, (2022).
Grant-Jacob J A, Praeger M, Eason R W, et al. ”Semantic segmentation of pollen grain images generated from scattering patterns via deep learning,” Journal of Physics Communications, vol. 5, no. 5, 055017 (11pp). (2021)
Yu C,Wang J, Peng C, et al. ”BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation,” ECCV 2018. Lecture Notes in Computer Science, vol. 11217, pp. 334-349. Springer, Cham. (2018).
Polat Z. ”Detection of Covid-19 from Chest CT Images using Xception Architecture: A Deep Transfer Learning based Approach,” Sakarya University Journal of Science, vol. 25, no. 3, pp. 813-823, (2021)
XiaoweiWang, Shoulin Yin, Ke Sun, et al. ”GKFC-CNN: Modified Gaussian Kernel Fuzzy Cmeans and Convolutional Neural Network for Apple Segmentation and Recognition,” Journal of Applied Science and Engineering, vol. 23, no. 3, pp. 555-561, (2020).
George B, Assaiya A, Roy R J, et al. ”CASSPER is a semantic segmentation-based particle picking algorithm for single-particle cryo-electron microscopy,” Communications Biology, vol. 4, no. 1. (2021)
Dai, Y., Xu, B., Yan, S., Xu, J.: Study of cardiac arrhythmia classification based on convolutional neural network. Computer Science and Information Systems, Vol. 17, No. 2, 445-458. (2020), https://doi.org/10.2298/CSIS191229011D
Ge, Y., Zhu, F., Huang, W., Zhao, P., Liu, Q.: Multi-Agent Cooperation Q-Learning Algorithm Based on Constrained Markov Game. Computer Science and Information Systems, Vol. 17, No. 2, pp. 647-664. (2020), https://doi.org/10.2298/CSIS191220009G
Wong C C, Yeh L Y, Liu C C, et al. ”Manipulation Planning for Object Re-Orientation Based on Semantic Segmentation Keypoint Detection,” Sensors, vol. 21, no. 7, 2280. (2021)
Guo X, Xiao R, Lu Y, et al. ”Cerebrovascular Segmentation from TOF-MRA based on Multiple-U-net with Focal Loss Function,” Computer Methods and Programs in Biomedicine, vol. 202, no. 3, pp. 105998. (2021)
Liu R, He D. ”Semantic Segmentation Based on Deeplabv3+ and Attention Mechanism,” 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, (2021).
H. Zhao, J. Shi, X. Qi, X. Wang and J. Jia. ”Pyramid Scene Parsing Network,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 6230-6239, doi: 10.1109/CVPR.2017.660.
J. Fu et al. ”Dual Attention Network for Scene Segmentation,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 3141-3149, doi: 10.1109/CVPR.2019.00326.
Chen LC., Zhu Y., Papandreou G., Schroff F., Adam H. ”Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,” ECCV 2018. Lecture Notes in Computer Science, vol 11211. Springer, Cham. (2018)
Z. Zhong et al. ”Squeeze-and-Attention Networks for Semantic Segmentation,” 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13062- 13071, (2020). doi:10.1109/CVPR42600.2020.01308.
Li X, Chen J, Ye Y, et al. ”Fast Semantic Segmentation Model PULNet and Lawn Boundary Detection Method,” Journal of Physics: Conference Series, vol. 1828, no. 1, pp. 012036 (16pp). (2021)
Trajanovski S, Shan C, Weijtmans P, et al.” Tongue Tumor Detection in Hyperspectral Images Using Deep Learning Semantic Segmentation,” IEEE Transactions on Biomedical Engineering, vol. 68, no. 4, pp. 1330-1340. (2021)
Wang K, Xiang K, Yang K. ”Polarization-driven Semantic Segmentation via Efficient Attention-bridged Fusion,” Optics Express, vol. 29, no. 4. (2021)