Yuan Xie
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Yuan Xie has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Kan Xie
School of Automation, Guangdong University of Technology, Guangzhou, China
2 shared publications - Shengli Xie
Guangdong University of Technology
2 shared publications - Hao-Xiang Wen
Shaoguan University
1 shared publication - Junjie Yang
School of Automation, Guangdong University of Technology, Guangzhou 510006, China
1 shared publication - Pu Li
School of Physics and Electromechanical Engineering, Shaoguan University, Shaoguan 512026, China
1 shared publication - Senquan Yang
School of Intelligent Engineering, Shaoguan University, Shaoguan, China
1 shared publication
Latest Publications
- 3 August 2021
- Published by Springer Nature in International Journal of Machine Learning and Cybernetics
- Vol. 12 (12) , 3573-3583
Underdetermined blind source separation of speech mixtures unifying dictionary learning and sparse representation
- 3 August 2021
- Published by Springer Nature in International Journal of Machine Learning and Cybernetics
- Vol. 12 (12) , 3573-3583
Underdetermined blind source separation of speech mixtures unifying dictionary learning and sparse representation
- Open Access
K-Hyperline Clustering-Based Color Image Segmentation Robust to Illumination Changes
Color image segmentation is very important in the field of image processing as it is commonly used for image semantic recognition, image searching, video surveillance or other applications. Although clustering algorithms have been successfully applied for image segmentation, conventional clustering algorithms such as K-means clustering algorithms are not sufficiently robust to illumination changes, which is common in real-world environments. Motivated by the observation that the RGB value distributions of the same color under different illuminations are located in an identical hyperline, we formulate color classification as a hyperline clustering problem. We then propose a K-hyperline clustering algorithm-based color image segmentation approach. Experiments on both synthetic and real images demonstrate the outstanding performance and robustness of the proposed algorithm as compared to existing clustering algorithms.
Open AccessK-Hyperline Clustering-Based Color Image Segmentation Robust to Illumination Changes
Color image segmentation is very important in the field of image processing as it is commonly used for image semantic recognition, image searching, video surveillance or other applications. Although clustering algorithms have been successfully applied for image segmentation, conventional clustering algorithms such as K-means clustering algorithms are not sufficiently robust to illumination changes, which is common in real-world environments. Motivated by the observation that the RGB value distributions of the same color under different illuminations are located in an identical hyperline, we formulate color classification as a hyperline clustering problem. We then propose a K-hyperline clustering algorithm-based color image segmentation approach. Experiments on both synthetic and real images demonstrate the outstanding performance and robustness of the proposed algorithm as compared to existing clustering algorithms.
- Open Access
Underdetermined Blind Source Separation Combining Tensor Decomposition and Nonnegative Matrix Factorization
Underdetermined blind source separation (UBSS) is a hot topic in signal processing, which aims at recovering the source signals from a number of observed mixtures without knowing the mixing system. Recently, expectation-maximization algorithm shows a great potential in the UBSS. However, the final separation results depend strongly on the parameter initialization, leading to poor separation performance. In this paper, we propose an effective algorithm that combines tensor decomposition and nonnegative matrix factorization (NMF). In the proposed algorithm, we first employ tensor decomposition to estimate the mixing matrix, and NMF source model is used to estimate the source spectrogram factors. Then a series of iterations are derived to update the model parameters. At the same time, the spatial images of source signals are estimated with Wiener filters constructed from the learned parameters. Therefore, time-domain sources can be obtained through inverse short-time Fourier transform. Finally, plenty of experimental results demonstrate the effectiveness and advantages of our proposed algorithm over the compared algorithms.
Open AccessUnderdetermined Blind Source Separation Combining Tensor Decomposition and Nonnegative Matrix Factorization
Underdetermined blind source separation (UBSS) is a hot topic in signal processing, which aims at recovering the source signals from a number of observed mixtures without knowing the mixing system. Recently, expectation-maximization algorithm shows a great potential in the UBSS. However, the final separation results depend strongly on the parameter initialization, leading to poor separation performance. In this paper, we propose an effective algorithm that combines tensor decomposition and nonnegative matrix factorization (NMF). In the proposed algorithm, we first employ tensor decomposition to estimate the mixing matrix, and NMF source model is used to estimate the source spectrogram factors. Then a series of iterations are derived to update the model parameters. At the same time, the spatial images of source signals are estimated with Wiener filters constructed from the learned parameters. Therefore, time-domain sources can be obtained through inverse short-time Fourier transform. Finally, plenty of experimental results demonstrate the effectiveness and advantages of our proposed algorithm over the compared algorithms.