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Structured Sparsity: Theory, Algorithms and Applications

.Introduction

This work investigates a new learning formulation called structured sparsity, which is a natural extension of the standard sparsity concept in
statistical learning and compressive sensing. By allowing arbitrary structures on the feature set, this concept generalizes the group sparsity idea. A general theory is developed for learning with structured sparsity, based on the notion of coding complexity associated with the structure. Moreover, efficient algorithms are developed to solve the structured sparsity problems. It is general enough to be applied in differnt applications to achieve bette performance.


.Theory

  1. Junzhou Huang, Tong Zhang, Dimitris Metaxas, "Learning with Structured Sparsity", The 26th International Conference on Machine Learning, ICML’09, Montreal, Quebec, Canada, June, 2009. [PDF] [SLIDES] [CODE]
  2. Junzhou Huang and Tong Zhang. "The Benefit of Group Sparsity", Annals of Statistics, Volume 38, Number 4, pp.1978-2004, August 2010. [PDF]
  3. Junzhou Huang, Tong Zhang and Dimitris Metaxas."Learning with Structured Sparsity", Journal of Machine Learning Research, Volume 12, pp. 3371-3412, November 2011 (Tech Report arXiv:0903.3002, March 2009). [PDF] [CODE]

Algorithms and Applications on Medical Imaging

  1. Zheng Xu, Sheng Wang, Yeqing Li, Feiyun Zhu and Junzhou Huang, "PRIM: An Efficient Preconditioning Iterative Reweighted Least Squares Method for Parallel Brain MRI Reconstruction", Neuroinformatics, Volume 16, Issue 3-4, pp. 425-430, October 2018. [CODE]
  2. Chen Chen, Lei He, Hongsheng Li and Junzhou Huang, "Fast Iteratively Reweighted Least Squares Algorithms for Analysis-Based Sparse Reconstruction", Medical Image Analysis, Volume 49, pp.141-152, October 2018. [CODE]
  3. Jiawen Yao, Zheng Xu, Xiaolei Huang and Junzhou Huang, “An Efficient Algorithm for Dynamic MRI Using Low-rank and Total Variation Regularization”, Medical Image Analysis, Volume 44, pp. 14-27, February 2018. [CODE]
  4. Chen Chen, Yeqing Li, Leon Axel and Junzhou Huang, “Real Time Dynamic MRI by Exploiting Spatial and Temporal Sparsity”, Magnetic Resonance Imaging, Volume 34, Issue 4, pp. 473-482, May 2016. [CODE]
  5. Zheng Xu, Yeqing Li, Leon Axel, Junzhou Huang, "Efficient Preconditioning in Joint Total Variation Regularized Parallel MRI Reconstruction", In Proc. of the 18th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI'15, Munich, Germany, October 2015. [CODE]
  6. Ruoyu Li, Yeqing Li, Ruogu Fang, Shaoting Zhang, Hao Pan, Junzhou Huang, "Fast Preconditioning for Accelerated Multi-Contrast MRI Reconstruction", In Proc. of the 18th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI'15, Munich, Germany, October 2015. (Oral Presentation)
  7. Jiawen Yao, Zheng Xu, Xiaolei Huang, Junzhou Huang, "Accelerated Dynamic MRI Reconstruction with Total Variation and Nuclear Norm Regularization", In Proc. of the 18th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI'15, Munich, Germany, October 2015. [CODE]
  8. Chen Chen, Fenghua Tian, Hanli Liu and Junzhou Huang, "Diffuse Optical Tomography Enhanced by Clustered Sparsity for Functional Brain Imaging", IEEE Transactions on Medical Imaging, Volume 33, Issue 12, pp. 2323-2331, December 2014. [CODE]
  9. Junzhou Huang, Chen Chen, Leon Axel, "Fast Multi-contrast MRI Reconstruction", Magnetic Resonance Imaging, Volume 32, Issue 10, pp. 1344–1352, December 2014. [CODE]
  10. Chen Chen and Junzhou Huang, ”Exploiting the wavelet structure in Compressed Sensing MRI”, Magnetic Resonance Imaging, Volume 32,Issue 10, pp. 1377–1389, December 2014. [CODE]
  11. Chen Chen, Yeqing Li, Leon Axel and Junzhou Huang, “Real Time Dynamic MRI with Dynamic Total Variation”, In Proc. of the 16th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI'14, Boston, USA, September 2014. [CODE]
  12. Chen Chen and Junzhou Huang, "The Benefit of Tree Sparsity in Accelerated MRI", Medical Image Analysis, Volume 18, Issue 6, pp. 834–842, August 2014. [CODE]
  13. Chen Chen, Yeqing Li, and Junzhou Huang, "Forest Sparsity for Multi-channel Compressive Sensing", IEEE Transactions on Signal Processing, Volume 62, Issue 11, pp. 2803-2813, June 2014. [CODE]
  14. Chen Chen, Yeqing Li and Junzhou Huang, "Calibrationless Parallel MRI with Joint Total Variation Regularization", In Proc. of the 16th Annual International Conf. on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2013. [CODE]
  15. Chen Chen and Junzhou Huang, "Compressive Sensing MRI with Wavelet Tree Sparsity", In Proc. of the 26th Annual Conference on Neural Information Processing Systems (NIPS), Nevada, USA, December 2012. [PDF] [CODE]
  16. Chen Chen, Junzhou Huang and Leon Axel, "Accelerated Parallel Magnetic Resonance Imaging with Joint Gradient and Wavelet Sparsity'', MICCAI Workshop on Sparsity Techniques in Medical Imaging, Nice, France, October 2012. [PDF]
  17. Chen Chen and Junzhou Huang, "The Benefit of Tree Sparsity in Accelerated MRI", MICCAI Workshop on Sparsity Techniques in Medical Imaging, Nice, France, October 2012. [PDF] (Best Paper Award)
  18. Junzhou Huang, Fei Yang, "Compressed Magnetic Resonace Imaging Based on Wavelet Sparsity and Nonlocal Total Variation". IEEE International Symposium on Biomedical Imaging, ISBI'12, Bacelona, Spain, May 2012. [PDF] [CODE]
  19. Junzhou Huang, Shaoting Zhang and Dimitris Metaxas, "Efficient MR Image Reconstruction for Compressed MR Imaging ", In Proc. of the 13th Annual International Conf. on Medical Image Computing and Computer Assisted Intervention, MICCAI’2010, Beijing, China, September 2010.. [PDF] [SLIDES] [CODE] [Supplemental] (MICCAI Young Scientist Award)
  20. Junzhou Huang, Zhen Qian, Xiaolei Huang, Dimitris Metaxas, Leon Axel, "Tag Separation in Cardiac Tagged MRI", In Proc. of the 11th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI'08, New York, USA, September 2008. [PROJECT]

Algorithms and Applications on Computer Vision

  1. Junzhou Huang, Xiaolei Huang, Dimitris Metaxas, "Simultaneous Image Transformation and Sparse Representation Recovery", In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'08, Anchorage, AL, USA, June 2008. [CODE] [PROJECT]
  2. Junzhou Huang, Xiaolei Huang, Dimitris Metaxas, "Learning with Dynamic Group Sparsity", The 12th International Conference on Computer Vision, Kyoto, Japan, October 2009. [SLIDES] [CODE] [PROJECT]
  3. Junzhou Huang, Shaoting Zhang and Dimitris Metaxas. " Fast Optimization for Mixture Prior Models ", In Proc. of the 11th European Conference on Computer Vision, ECCV'10, Crete, Greece, September 2010. [CODE]
  4. Baiyang Liu, Lin Yang, Junzhou Huang, Peter Meer, Leiguang Gong, Casimir Kulikowski, "Robust and Fast Collaborative Tracking with Two Stage Sparse Optimization", In Proc. of the 11th European Conference on Computer Vision, ECCV'10, Crete, Greece, September 2010.
  5. Shaoting Zhang, Junzhou Huang,Yuchi Huang, Yang Yu, Hongsheng Li, Dimitris Metaxas, "Automatic Image Annotation Using Group Sparsity", In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'10, San Francisco, USA, June 2010. Oral presentation. [CODE]
  6. Baiyang Liu, Junzhou Huang, Casimir Kulikowski, Lin Yang, "Robust Tracking Using Local Sparse Appearance Model and K-Selection", In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'11, Colorado Springs, Colorado, USA, June 2011. Oral presentation.
  7. Shaoting Zhang, Yiqiang Zhan, Maneesh Dewan, Junzhou Huang, Dimitris Metaxas and Xiang Zhou, "Sparse Shape Composition: A New Framework for Shape Prior Modeling", In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'11, Colorado Springs, Colorado, USA, June 2011.
  8. Xinyi Cui, Junzhou Huang, Shaoting Zhang and Dimitris Metaxas, "Background Subtraction using Group Sparsity and Low Rank Constraint", In Proc. of the 12th European Conference on Computer Vision, ECCV'12, Firenze, Italy, October 2012.
  9. Lin Zhong, Qingshan Liu, Peng Yang, Bo Liu, Junzhou Huang and Dimitris Metaxas, "Learning Active Facial Patches for Expression Analysis". In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'12, Providence, Rhode Island, USA, June 2012.
  10. Shaoting Zhang, Junzhou Huang, Hongsheng Li and Dimitris Metaxas, "Automatic Image Annotation and Retrieval Using Group Sparsity". IEEE Transactions on Systems, Man, and Cybernetics: Part B (TSMC), Volume 42, Issue 3, pp. 838-849, 2012. [CODE]
  11. Baiyang Liu, Junzhou Huang, Casimir Kulikowski, Lin Yang, "Robust Visual Tracking Using Local Sparse Appearance Model and K-Selection", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, Issue 12, pp. 2968 - 2981, December 2013.
  12. Shaoting Zhang, Yiqiang Zhan, Xinyi Cui, Mingchen Gao, Junzhou Huang, Dimitris Metaxas, "3D Anatomical Shape Atlas Construction using Mesh Quality Preserved Deformable Models", Computer Vision and Image Understanding, Volume 117, Issue 9, pp. 1061-1071, September 2013.
  13. Chen Chen, Junzhou Huang, Lei He, and Hongsheng Li, "Preconditioning for Accelerated Iteratively Reweighted Least Squares in Structured Sparsity Reconstruction", In Proc. of IEEE Conference on Computer Vision and Pattern Recognition, CVPR'14, Columbus, Ohio, USA, June 2014. (Oral Presentation) [CODE]
  14. Chen Chen, Yeqing Li, Wei Liu, and Junzhou Huang, "Image Fusion with Local Spectral Consistency and Dynamic Gradient Sparsity", In Proc. of IEEE Conference on Computer Vision and Pattern Recognition, CVPR'14, Columbus, Ohio, USA, June 2014. (Oral Presentation) [CODE]
  15. Yeqing Li, Chen Chen, Fei Yang and Junzhou Huang, "Deep Sparse Representation for Robust Image Registration", In Proc. of IEEE Conference on Computer Vision and Pattern Recognition, CVPR'15, Boston, USA, June 2015. [CODE]

 


Useful Sources on Related Topics

Compressive Sensing Resources

Nuit Blanche

The Lasso page

Sparse Learning Package


 

Structured Sparsity Examples

Tree sparsity Tree sparsity in Wavelets Graph sparsity Graph sparsity in foreground images

 

Motivations: History of Prior Models

Figure. Evolution of prior models