Welcome to my homepage
for Machine Vision and Signal Analysis,
University of Oulu, Finland
Li Liu received the B.S. degree in communication engineering, the M.S. degree in photogrammetry and remote sensing and the Ph.D. degree in information and communication engineering from the National University of Defense Technology, China, in 2003, 2005 and 2012, respectively. During her PhD study, she spent more than two years as a Visiting Student at the University of Waterloo, Canada, from 2008 to 2010. From 2015 to 2016, she visited the Multimedia Laboratory at the Chinese University of Hong Kong. Since 2016.12, she has been working at the Center for Machine Vision and Signal analysis of the University of Oulu, Finland.
· CFP: CroMoL 2019—1st International Workshop@ICCV 2019 on Cross-Modal Learning in Real World
Texture Analysis, Image Classification, Object detection, Scene understanding, Facial Analysis
Zhuo Su (2018-2022)
Wanxia Deng (2018-2020)
· National Natural Science Foundation of China, Principal Investigator.
· The Hunan Provincial Natural Science Fund for Distinguished Young Scholars, Principal Investigator.
· Excellent Young Scholar Award in National University of Defense Technology, Principal Investigator.
· National Natural Science Foundation of China, Random Projection based Texture Feature and its Applications, Principal Investigator, 2013-2015.
· Open Projects Program of National Laboratory of Pattern Recognition 2015-2016.
Selected Publications (For my recent publications, please refer to my Google Scholar Page):
Publications in 2019
1. (New) SwapGAN: A Multi-stage Generative Approach for Person-to-Person Fashion Style Transfer, Yu Liu, Wei Chen, Li Liu, Michael S. Lew, IEEE Transactions on MultiMedia, 2019.
2. (New) Dynamic Texture Classification Using Unsupervised 3D Filter Learning and Local Binary Encoding, Xiaochao Zhao, Yaping Lin, Li Liu, Janne Heikkila, Wenming Zheng, IEEE Transactions on MultiMedia, 2019.
3. (New) Texture Classification in Extreme Scale Variations using GANet, Li Liu, Jie Chen, Guoying Zhao, Paul Fieguth, Xilin Chen, Matti Pietikanen, IEEE Transactions on Image Processing, 2019.
Publications in 2018
1. (New) Deep Learning for Generic Object Detection: A Survey, Li Liu, Wanli Ouyang, Xiaogang Wang, P. Fieguth, J. Chen, X. Liu, M. Pietikainen, International Journal of Computer Vision (Under Review), 2018 https://arxiv.org/abs/1809.02165.
2. (New) From BoW to CNN: Two Decades of Texture Representation for Texture Classification, Li Liu, J. Chen, P. Fieguth, G. Zhao, R. Chellappa, M. Pietikainen, International Journal of Computer Vision, 2018, Accepted. (PDF download)
3. (New) Absent Multiple Kernel Learning Algorithms, X. Liu, L. Wang, X. Zhu, M. Li, E. Zhu, T. Liu, Li Liu and J. Yin, IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2018 (PDF Download).
4. (New) Texture Classification in Extreme Scale Variations using GANet, Li Liu, J. Chen, G. Zhao, P. Fieguth, X. Chen, M. Pietikäinen, https://arxiv.org/abs/1802.04441, 1-10, 2018. (IEEE TIP Major Revision).
6. Learning Visual and Textual Representations for Multimodal Matching and Classification, Yu Liu, Li Liu, Yanming Guo, and Machael S. Lew, Pattern Recognition, 2018. (PDF download)
7. A Dual Prediction Network For Image Captioning, Y. Guo, Y. Liu, M. Boer, Li Liu, M.S. Lew, IEEE International Conference on Multimedia and Expo (ICME), 2018.
8. Unsupervised Cross Corpus Speech Emotion Recognition Using Domain Adaptive Subspace Learning, N. Liu, Y. Zong, B. Zhang, Li Liu, J. Chen, G. Zhao, J. Zhu, ICASSP, 2018.
9. Super Wide Regression Network For Unsupervised Cross Database Facial Expression Recognition, N. Liu, B. Zhang, Y. Zong, L. Liu, J. Chen, G. Zhao, J. Zhu, ICASSP, 2018.
10. Rotation Invariant Local Binary Convolution Neural Networks, X. Zhang, Y. Xie, J. Chen, L. Wu, Q. Ye, Li Liu, IEEE Access, 6, 18420-18430, 2018.
11. 自然场景图像中的文字检测综述, 王润民, 桑农, 丁丁, 陈杰, 叶齐祥, 高常鑫, 刘丽, 自动化学报, 2018.
Publications in 2017
1. Li Liu, Paul Fieguth, Yulan Guo, Xiaogang Wang, and Matti Pietikainen, Local Binary Features for Texture Classification: Taxonomy and Experimental Study, Pattern Recognition, vol. 62, pp. 135-160, 2017. (PDF)
2. J. Chen, V. Patel, Li Liu, V. Kellokumpu, G. Zhao, M. Pietikäinen, R. Chellappa, Robust Local Feature for Remote Face Recognition, Image and Vision Computing 64, 34-46 3 2017 . (PDF)
3. X. Zhang, Li Liu, Y. Xie, J. Chen, L. Wu, M. Pietikainen, Rotation Invariant Local Binary Convolution Neural Networks, International Conference on Computer Vision Workshop, 2017.
Journal Papers (Up to 2016):
Conference Papers (Up to 2016)::
1. Li Liu, Paul Fieguth, Xiaogang Wang, and Matti Pietikainen, Evaluation of LBP and Deep Texture Descriptors with A New Robustness Benchmark, ECCV, 2016.
2. Li Liu, Paul Fieguth, Matti Pietikainen and Songyang Lao, Median Robust Extended Local Binary Pattern for Texture Classification, IEEE International Conference on Image Processing (ICIP), 2015, Oral Presentation.
3. Li Liu, Paul Fieguth, Guoying Zhao and Matti Pietikainen, Extended Local Binary Pattern Fusion for Face Recognition, IEEE International Conference on Image Processing (ICIP), 2014, Oral Presentation.
4. Li Liu, Bing Yang, Paul Fieguth, Zheng Yang and Yingmei Wei, BRINT: A Binary Rotation Invariant And Noise Tolerant Texture Descriptor, IEEE International Conference on Image Processing (ICIP), 2013.
5. Li Liu, Paul Fieguth, Gangyao Kuang and Hongbin Zha, Sorted Random Projections for Robust Texture Classification, International Conference on Computer Vision (ICCV), 2011.
6. Li Liu, Paul Fieguth and Gangyao Kuang, Compressed Sensing for Robust Texture Classification, Asian Conference on Computer Vision (ACCV), 2010, Oral Presentation.
7. Li Liu, Paul Fieguth and Gangyao Kuang, Combining Sorted Random Features for Texture Classification, International Conference on Image Processing (ICIP), 2011.
8. Li Liu, Paul Fieguth and Gangyao Kuang, Generalized Local Binary Patterns for Texture Classification, British Machine Vision Conference (BMVC), 2011.
18. Li Liu and Paul Fieguth, Texture Classification Using Compressed Sensing, Canadian Conference on Computer and Robot Vision (CRV), 2010.
19. Shuxuan Guo, Li Liu, Wei Wang, Songyang Lao, Liang Wang, An Attention Model Based on Spatial Transformers for Scene Recognition, International Conference on Pattern Recognition, 2016.
20. NaWang, Li Liu, Lingjun Zhao and Jun Lu, A novel Polarimetric SAR ship detection method, Asia-Pacific International Conference on Synthetic Aperture Radar (APSAR), 2011.
21. Wei Wang, Li Liu, Yongmei Jiang and Gangyao Kuang, Point-based Rigid Registration, Asiap-acific International Conference on Synthetic Aperture Radar (APSAR), 2011.
Editorial and Cochair
· Associate Editor of The Visual Computer Journal
· Guest Editor of Special Issue RoLoD: Robust local descriptors for computer vision for the journal of Neurocomputing
· Guest Editor of Special Issue Compact and Efficient Feature Representation and Learning in Computer Vision for the journal of IEEE Transactions on Pattern Analysis and Machine Intelligence
· CoChair of the International Workshop on RoLoD: Robust local descriptors for computer vision at ACCV2014
· CoChair of the International Workshop on RoF: Robust Features for Computer Vision at CVPR2016
· CoChair of the International Workshop on Compact and Efficient Feature Representation and Learning in Computer Vision at ICCV2017
Journal Paper Review
· IEEE Transactions on Pattern Analysis and Machine Intelligence
· IEEE Transactions on Image Processing
· IEEE Transactions on Circuits and Systems for Video Technology
· IEEE Transactions on Signal Processing
· IEEE Transactions on Geosciences Remote Sensing
· Pattern Recognition
· Image and Vision Computing
· Computer Vision and Image Understanding
Conference Paper Review
· Internal Conference of Computer Vision and Pattern Recognition
· European Conference on Computer Vision
· International Conference on Computer Vision
· International Conference on Image Processing
· Asian Conference on Computer Vision
Last Update: February 18, 2019