My name is Ligang Zheng. I am a faculty at school of computer science, Guangzhou University, since Sep. 2012. Before that, I got my PhD at the school of information science and technology, Sun-Yat Sen university(SYSU) in Jul. 2012. 

My PhD & MS tutor is Prof. Jiwu Huang, IEEE Fellow. During my PhD, I visited the school of computer science, the university of Nottingham, where my tutor is Prof. Guoping Qiu. From Sep.2016 to Aug. 2017, I visited the school of Computer Science (Tutor, prof. Vikas Singh), the university of  Wisconsins - Madison.  Both visitings are sponsored the China Scholarship Council (thanks to CSC).

I won a Best Student Paper Award Nominated in ICIP 2011, Brussels, Belgium.

Research Interest

Apply Machine Learning  and Statistics to solve practical problems in large scale multimedia content analysis. 

Academic visiting

2018.8,                                  The center for future media, University of Electronic Science and Technology of China

2009.4-2009.5,                     School of Computer science, Tsinghua University

2009.11-2010.11,                 School of Computer science, the University of Nottingham         

2016.9- 2017.8,                    School of Computer Science, the University of Wisconsins - Madison

Academic Service:

Reviewer for: 

IEEE Transactions on Neural Networks and Learning System(TNNLS), 

IEEE Transactions on Information Forensics and Security(TIFS), 

The Journal of software (软件学报)

Reviewer for NSFC

Grant

1: Salient covariance and its application in content based video detection, Supported by National Natural Science Foundation of China (NSFC), 220K (Chinese Yuan), 2014.1 - 2016.12

2: SPD manifold for Multimedia content analysis and statistics, Supported by Shenzhen Key Laboratory of Media Security, Shenzhen University, 2016

3: The Statistics and Inferenace model in Non-Euclidean manifolds,a sub-project supported by High-level University Program of Guangdong Province,2015

4: Novel techniques for near-duplicate Image detection in Riemannian manifold, Supported by seedling project for excellent young researchers, Guangzhou University 2014

5: Guangzhou Univesity Start-Up Funding, 2013

6: Fast and accurate nearest neighbor search in SPD manifolds. Supported by Guangzhou Bureau of Education, 2017

Selected Publication

1. Ligang Zheng, Jiwu Huang and Guoping Qiu, Riemannian competitive learning for SPD matrices clustering, Nerocomputing, 2018, 295,153-164

2. Ligang Zheng, Chao Chen: Fast Near-duplicate Image Detection in Riemannian Space by a Novel Hashing Scheme, ICCCS 2018

3. Ligang Zheng, Yanqiang Lei, Guoping Qiu and Jiwu Huang, Near-Duplicate image detection in a visually salient Riemannian space, IEEE Transactions on Information Forensics and Security(TIFS) 7(5): 1578-1593 (2012) 

4. Ligang Zheng, Guoping Qiu and Jiwu Huang,  Efficient coarse-to-fine near-duplicate image detection in riemannian manifold, ICASSP 2012: 977-980 (Kyoto, Japan, Oral)

5. Ligang Zheng, Guoping Qiu, Jiwu Huang and Hao Fu,  Salient covariance for near-duplicate image and video detection, ICIP 2011: 2537-2540 (Best Student paper Award Nominated) (Brussels,Belgium,Oral)

6. Ligang Zheng, Guoping Qiu and Jiwu Huang, Fast and Accurate Nearest Neighnour Search in the Manifolds of Symmetric Positive Definite Matrices, ICASSP 2014: 3804-3808 (Florence, Italy,Poster)

7. Yanqiang Lei, Ligang Zheng and Jiwu Huang,  Geometric Invariant Features in Radon Transform domain for near-duplicate image detection, Pattern Recognition (PR)47(11): 3630-3640 (2014) 

8. Yanqiang Lei, Guoping Qiu, Ligang Zheng and Jiwu Huang, Fast Near-Duplicate Image Detection Using Uniform Randomized Trees, ACM Transactions on Multimedia Computing, Communications and Application(ACM TOMM)10(4): 35 (2014)

9. Ligang Zheng, Guoping Qiu and Jiwu Huang, Clustering Symmetric Positive Definite Matrices on the Riemannian Manifolds, ACCV 2016, pp.400-415 (Accepted rate <25%, Taipei, Taiwan,Poster)

10. Ligang Zheng, Hyunwoo J Kim, Nagesh Adluru, Michael A Newton, Vikas Singh, Riemannian Variance Filtering: An Independent Filtering Scheme for Statistical Tests on Manifold-valued Data, 2017  CVPRW, pp.699-708 (Honolulu, Hawaii,Oral)

Teaching

Artificial Intelligence

Probability Statistics 

Computer Networks

Machine Learning