Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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Unified Sparse Coding Framework (USCF)


Sparse Coding is recently employed in many research areas of Image processing such as classification, transfer learning, cross-modalities, and hashing techniques. One powerful toolbox, SPAMS [1], implemented different algorithms of DL and SC, however, many advanced algorithms such as Graph Sparse Coding [2] and Transfer Sparse Coding [3] have not implemented yet. Former students of Machine Learning course implemented successfully the module of cross-modality learning in this framework. Further, they improved the performance of some implemented algorithms. In this course, we are interested in implementing Transfer Sparse Coding algorithms as well as Feature Learning algorithms, in particular, [4].

Project Phases

Phase Period Conducted by Materials Results so far
Sparse Coding and Dictionary Learning April all Slides I
Slides II

What you are going to learn:

  • Sparse Coding and Dictionary Learning
  • ...

Programming Language


Useful Links


  • [1] J. Mairal Sparse Modeling for Image and Vision Processing. Foundations and Trends in Computer Graphics and Vision. Vol 8. No. 2-3. Pp. 85--283. 2014
  • [2] Zheng, "Graph Regularized Sparse Coding for Image Representation," Image Processing, IEEE Transactions on. Vol.20. No.5. Pp.1327-1336, 2011
  • [3] Long; "Transfer Sparse Coding for Robust Image Representation," Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pp.407-414, 23-28 June 2013
  • [4] Kiro. Et. Al, “Stacked Multiscale Feature Learning for Domain Independent Medical Image Segmentation”, Machine Learning in Medical Imaging, 2014.
  • [5] M. Fowler, Refactoring: Improving the Design of Existing Code.

Edit | Attach | Refresh | Diffs | More | Revision r1.3 - 30 Apr 2015 - 08:04 - ShadiAlbarqouni