Grants and Awards

·       2017 Community Engagement Grant, University of Wollongong (AUD8,000)

Retrieving lost community stories: linking regional archival photo collections using advanced visual technologies 

Role: Lead Chief Investigator

·       2017, 2016 National Computational Merit Allocation Scheme

Exploring National Treasure: Retrieval of Large Collection of Archival Photos 

Role: Lead Chief Investigator

·       2014-15 Commercial Research Project with an IT Company in US (AUD182,000)

Clothing Image Retrieval Research

Role: Lead Chief Investigator

·       2013 Near Miss Grant for ARC Discovery Project, University of Wollongong (AUD10,000)

Role: Lead Chief investigator

·       2012 Near Miss Grant for ARC Discovery Project, University of Wollongong (AUD12,000)

Role: Lead Chief investigator

·       2012 URC Small Grant, University of Wollongong University Research Committee (AUD13,000)

When Vision Meets Fashion: Addressing The Problem of Fine-grained Image Retrieval

Lei Wang and Markus Hagenbuchner

Role: Lead Chief investigator

·       2011 Research Develop Scheme Grant, University of Wollongong Faculty of Informatics (AUD3,500)

Lei Wang

Role: Sole investigator

·       2011 Research Infrastructure Block Grant, University of Wollongong (AUD70,000)

3D Multimedia Research Infrastructure

Role: Coordinator / Primary User

·       2009 Early Career Researcher Award, Australian Academy of Science Australian Research Council

·       2009 ARC Linkage Project, Australian Research Council (AUD240,000 from ARC, 2009—2012)

Generic Content-based Picture Retrieval with Its Application to Archival Photographic Collections

Lei Wang, Richard Hartley, and Hongdong Li

Role: Lead Chief investigator

·       2007 ARC Discovery Project, Australian Research Council (AUD255,000, 2007—2009 )

Computer Vision Optimization Problem Using Machine Learning

Richard Hartley and Lei Wang

Role: Australian Postdoctoral Fellowship (APD)

 

 

Selected publication (a full list of publication)

1.     L. Wang, L. Liu, L. Zhou and K. Chan, Application of SVMs to the Bag-of-Features Model: A Kernel Perspective, book Support Vector Machines Application, published by Springer in January 2014.

2.     L. Zhou, L. Wang, L. Liu, P. Ogunbona and D. Shen, Support Vector Machines for Neuroimage Analysis: Interpretation from Discrimination, book Support Vector Machines Application, published by Springer in January 2014.


3.     W. Li, Y. Gao, L. Wang, L. Zhou, J. Huo, and Y. Shi, OPML: A One-Pass Closed-Form Solution for Online Metric Learning, Pattern Recognition, Accepted in Mar 2017.

4.     L. Liu, P. Wang, C. Shen, L. Wang, A .Van den Hengel, C. Wang, and H. Shen, Compositional Model based Fisher Vector Coding for Image Classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, Accepted in Dec 2016.

5.     S. Huang, J. Zhang, D. Schonfeld, L. Wang, and X. Hua, Two-Stage Friend Recommendation Based on Network Alignment and Series-Expansion of Probabilistic Topic Model, IEEE Transactions on Multimedia, Accepted in Dec 2016.

6.     J. Zhang, L. Zhou and L. Wang, Subject-adaptive Integration of Multiple SICE Brain Networks with Different Sparsity, Pattern Recognition, Accepted in Sep 2016.

7.     Z. Gao, L. Wang, L. Zhou, and J. Zhang, HEp-2 Cell Image Classification with Deep Convolutional Neural Networks, IEEE Journal on Biomedical and Health Informatics (originally IEEE Transactions on Information Technology in Biomedicine), Accepted in Jan 2016.

8.     L. Zhou, L. Wang, L. Liu, P. Ogunbona, and D. Shen, Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data, IEEE Transactions on Pattern Analysis and Machine Intelligence, Accepted in Nov 2015.

9.     L. Wang, L. Liu, and L. Zhou, A Graph-embedding Approach to Hierarchical Visual Word Mergence, IEEE Transactions on Neural Networks and Learning Systems, Accepted in Dec 2015.

10.  S. Huang, J. Zhang, L. Wang, and X. Hua, Social Friend Recommendation Based on Multiple Network Correlation, IEEE Transactions on Multimedia, Accepted in Dec 2015.

11.  H. Ni, L. Zhou, X. Ning, and L. Wang, Exploring Multifractal-based Features for Mild Alzheimer’s Disease Classification, Magnetic Resonance in Medicine, Accepted in July 2015.

12.  J. Zhang, L. Wang, L. Zhou, and W. Li, Learning Discriminative Stein Kernel for SPD Matrices and Its Applications, IEEE Transactions on Neural Networks and Learning Systems, Accepted in May 2015.   

13.  L. Liu, L. Wang, and C. Shen, A Generalized Probabilistic Framework for Compact Codebook Generation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Accepted in April 2015. 

14.  C. Wang, L. Wang, and L. Liu, Density Maximization for Improving Graph Matching with Its Applications, IEEE Transactions on Image Processing, Accepted in March 2015.

15.  J. Zhang, L. Zhou, L. Wang, and W. Li, Functional Brain Network Classification With Compact Representation of SICE Matrices, IEEE Transactions on Biomedical Engineering, Accepted in January 2015. 

16.  X. Liu, L. Zhou, L. Wang, J. Zhang, J. Yin and D. Shen, An Efficient Radius-incorporated MKL Algorithm for Alzheimer’s Disease Prediction, Pattern Recognition, Accepted in December 2014.

17.  X. Liu, L. Wang, J. Zhang, J. Yin and H. Liu. Global and Local Structure Preservation for Feature Selection, IEEE Transactions on Neural Networks and Learning Systems, 25(6):1083-1095, June 2014.

18.  L. Liu and L. Wang. HEp-2 Cell Image Classification with Multiple Linear Descriptors, Pattern Recognition, 47(7):2400-2408, July 2014.

19.  H. Tian, W. Li, L. Wang and P. Ogunbona. Smoke Detection in Video: An Image Separation Approach, International Journal of Computer Vision, 106(2):192-209, January 2014.

20.  L. Wang, L. Zhou, C. Shen, L. Liu and H. Liu. A Hierarchical Word-merging Algorithm with Class Separability Measure, IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(3):417-435, March 2014. 

21.  C. Shen, J. Kim, F. Liu, L. Wang, and A .Van den Hengel, Efficient Dual Approach to Distance Metric Learning, IEEE Transactions on Neural Networks and Learning Systems, 25(2):394-406 February 2014.

22.  X. Liu, L. Wang, J. Yin, E. Zhu and J. Zhang, An Efficient Approach to Integrating Radius Information into Multiple Kernel Learning, IEEE Transactions on Cybernetics, 43(2):557-569, April 2013.

23.  Z. Zhao, L. Wang, H. Liu and J. Ye. On Similarity Preserving Feature Selection. IEEE Transactions on Knowledge and Data Engineering, 25(3):619-632, March 2013.

24.  X. Liu, J. Yin, L. Wang, L. Liu, J. Liu, C. Hou and J. Zhang, An Adaptive Approach to Learning Optimal Neighbourhood Kernels, IEEE Transactions on Cybernetics, 43(1):371-384, February 2013.

25.  C. Shen, J. Kim, L. Wang, and A .Van den Hengel. Positive Semidefinite Metric Learning Using Boosting-like Algorithms. Journal of Machine Learning Research, 13:1007-1036, 2012.

26.  L. Zhou, L. Wang, and C. Shen. Feature Selection with Redundancy-constrained Class Separability. IEEE Transactions on Neural Networks, 21(5):853-858, May 2010.

27.  C. Shen, J. Kim, and L. Wang. Scalable Large-margin Mahalanobis Distance Metric Learning. IEEE Transactions on Neural Networks, 21(9):1524-1530, September 2010.

28.  L. Zhou, R. Hartley, L. Wang, P. Lieby and N. Barnes. Identifying Anatomical Shape Difference by Regularized Discriminative Direction. IEEE Transactions on Medical Imaging, 28(6):937-950, June 2009.

29.  L. Wang, Feature Selection with Kernel Class Separability, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(9):1534–1546, September, 2008.

30.  L. Wang, K. L. Chan, P. Xue and L. Zhou. A Kernel-induced Space Selection Approach to Model Selection of KLDA. IEEE Transactions on Neural Networks, 19(12):2116-2131, December 2008.

31.  L. Wang, K. L. Chan, and P. Xue. Two criteria for Model Selection of Multi-class Support Vector Machines. IEEE Transactions on Systems, Man and Cybernetics, Part B, 38(6):1432-1448, December 2008.

32.  H. Kong, L. Wang, E. K. Teoh, X. Li, J.-G. Wang, and R. Venkateswarlu. Generalized 2D principal component analysis for face image representation and recognition. Neural Networks, 18(5-6):585–594, July-August 2005.

33.  L. Wang, K. L. Chan, and P. Xue. A criterion for optimizing kernel parameters in KBDA for image retrieval. IEEE Transactions on Systems, Man and Cybernetics, Part B, 35(3):556–562, June 2005.


 

34.  L. Zhou, L. Wang, J. Zhang, Y. Shi and Y. Gao, Revisiting Distance Metric Learning for SPD Matrix based Visual Representation, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), July 2017.

 

35.  M. Zieba and L. Wang, Training Triplet Networks with GAN, Workshop Track of the 5th International Conference on Learning Representations (ICLR), April 2017.

 

36.  X. Liu, M. Li, L. Wang, Y. Dou, J. Yin, and E. Zhu, Multiple Kernel k-Means with Incomplete Kernels, The Thirty-first AAAI Conference on Artificial Intelligence (AAAI), February 2017.

 

37.  M. Li, X. Liu, L. Wang, Y. Dou, J. Yin, and E. Zhu, Multiple Kernel Clustering with Local Kernel Alignment Maximization, The Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI), July 2016. (Code)

 

38.  X. Liu, Y. Dou, J. Yin, L. Wang, and E. Zhu, Multiple Kernel k-Means Clustering with Matrix-induced Regularization, The Thirtieth AAAI Conference on Artificial Intelligence (AAAI), February 2016. (Code)

 

39.  L. Wang, J. Zhang, L. Zhou, C. Tang and W. Li, Beyond Covariance: Feature Representation with Nonlinear Kernel Matrices, IEEE International Conference on Computer Vision (ICCV), December 2015. (Code)

 

40.  X. Liu, L. Wang, J. Yin, D. Yong and J. Zhang, Absent Multiple Kernel Learning, The Twenty-ninth AAAI Conference on Artificial Intelligence (AAAI), January 2015.

 

41.  L. Liu, C. Shen, L. Wang, A .Van den Hengel and C. Wang. Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors, Proceedings of Advances in Neural Information Processing Systems (NIPS), September 2014.

 

42.  C. Wang, L. Wang and L. Liu. Progressive Mode-seeking for Fast Sparse Feature Matching, In the 13th European Conference on Computer Vision (ECCV), September 2014. (Oral presentation, Code)

43.  L. Zhou, L. Wang, L. Liu, P. Ogunbona and D. Shen. Max-margin Based Learning for Discriminative Bayesian Network from Neuroimaging Data, In the 17th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), September 2014.

44.  X. Liu, L. Wang, J. Zhang and J. Yin. Sample-adaptive Multiple Kernel Learning, The Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI), July 2014.

45.  L. Zhou, L. Wang and P. Ogunbona. Discriminative Sparse Inverse Covariance Matrix: Application in Brain Functional Network Classification, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2014.

46.  C. Wang, L. Wang and L. Liu. Improving Graph Matching via Density Maximization, IEEE International Conference on Computer Vision (ICCV), December 2013.

47.  L. Liu and L. Wang. A Scalable Unsupervised Feature Merging Approach to Efficient Dimensionality Reduction of High-dimensional Visual Data, IEEE International Conference on Computer Vision (ICCV), December 2013.

48.  L. Wang, J. Zhang, L. Zhou and W. Li. A Fast Approximate AIB Algorithm for Distributional Word Clustering, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2013.

49.  L. Zhou, L. Wang, L. Liu, P. Ogunbona and D. Shen. Discriminative Brain Effective Connectivity Analysis for Alzheimer's Disease: A Kernel Learning Approach upon Sparse Gaussian Bayesian Networks, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2013.

50.  L. Liu and L. Wang. What Has My Classifier Learned? Visualizing the Classification Rules of Bag-of-Feature Model by Support Region Detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2012.

51.  L. Liu and L. Wang. Exploring Latent Class Information for Image Retrieval Using the Bag-of-Feature Model, the 20th ACM International Conference on Multimedia (ACMMM), November 2011.

52.  L. Liu, L. Wang, and X. Liu. In Defence of Soft-assignment Coding, IEEE International Conference on Computer Vision (ICCV), November 2011.

53.  L. Liu, L. Wang, and C. Shen. A Generalized Probabilistic Framework for Compact Codebook Creation, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2011.

54.  C. Shen, J. Kim, and L. Wang. A Scalable Dual Approach to Semidefinite Metric, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June 2011.

55.  L. Zhou, L. Wang, C. Shen, and N. Barnes. Hippocampal Shape Classification Using Redundancy Constrained Feature Selection, In the 13th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), September 2010.

56.  Z. Zhao, L. Wang, and H. Liu. Efficient Spectral Feature Selection with Minimum Redundancy. In Proceedings of Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI), July 2010. (Oral Presentation)

57.  Y. Zhang, R. Hartley, and L. Wang. Fast Multi-labelling for Stereo Matching, the 11th European Conference on Computer Vision (ECCV), September 2010.

58.  C. Shen, J. Kim, L. Wang, and A .Van den Hengel. Positive semidefinite metric learning with Boosting. Proceedings of Advances in Neural Information Processing Systems (NIPS), December 2009.

59.  C. Shen, A. Welsh, and L. Wang. PSDBoost: Matrix-generation linear programming for positive semidefinite matrices learning. Proceedings of Advances in Neural Information Processing Systems (NIPS), December 2008.

60.  L. Wang, L. Zhou, and C. Shen. A Fast Algorithm for Creating A Compact and Discriminative Visual Codebook. Lecture Notes in Computer Science, Springer, the 10th European Conference on Computer Vision (ECCV), pages 719-732, October 2008.

61.  L. Zhou, R. Hartley, L. Wang, P. Lieby and B. Nick. Regularized Discriminative Direction for Shape Difference Analysis. Lecture Notes in Computer Science, Springer, the 11th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pages 628-635, September 2008.

62.  L. Wang. Toward A Discriminative Codebook: Codeword Selection across Multiresolution. the 2nd Beyond Patches Workshop, in conjunction with International Conference on Computer Vision and Pattern Recognition (CVPR), June 2007. (Oral presentation)

63.  L. Wang, X. Li, P. Xue, and K. L. Chan. A novel framework for SVM-based image retrieval on large databases. In Proceedings of the 13th ACM International Conference on Multimedia (ACMMM), pages 487–490, November 2005.

64.  L. Wang, Y. Gao, K. L. Chan, P. Xue, and W.-Y. Yau. Retrieval with knowledge-driven kernel design: an approach to improving SVM-based CBIR with relevance feedback. In Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV), pages 1355–1362, October 2005.

65.  X. Li, L. Wang, and E. Sung. A study of adaboost with SVM based weak learners. In Proceedings of International Joint Conference on Neural Networks (IJCNN), Québec, Canada, pages 196–201(1), August 2005. (Oral presentation)

66.  H. Kong, X. Li, L. Wang, E. K. Teoh, J.-G. Wang, and R. Venkateswarlu. Generalized 2D principal component analysis. In Proceedings of International Joint Conference on Neural Networks (IJCNN), Qu´ebec, Canada, pages 108-113(1), August 2005. (Oral presentation)

67.  H. Kong, L. Wang, E. K. Teoh, J.-G. Wang, and R. Venkateswarlu. A framework of 2D fisher discriminant analysis: application to face recognition with small number of training samples. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 1083–1088, June 2005.

68.  L. Wang, K. L. Chan, and Z. Zhang. Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 16-22 June 2003, Madison, WI, USA, pages 629–634, 2003. (Oral presentation)

69.  L. Wang and K. L. Chan. Learning kernel parameters by using class separability measure. In The sixth kernel machines workshop, in conjunction with Neural Information Processing Systems (NIPS), Whistler, Canada, 2002.          

 

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