1. L1_APG (Matlab, ~40M with data), the code implement the L1-APG tracker described in the following paper:
   [1] C. Bao, Y. Wu, H. Ling and H. Ji, "Real time robust L1 tracker using accelerated proximal gradient approach", IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Rhode Island, 2012. (PDF)
   The paper extends the following two papers:
   [2] X. Mei, H. Ling, Y. Wu, E. Blasch, and L. Bai, "Minimum Error Bounded Efficient L1 Tracker with Occlusion Detection", IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, 2011. (PDF)
   [3] X. Mei and H. Ling, "Robust Visual Tracking and Vehicle Classification via Sparse Representation", IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 33(11):2259--2272, 2011. (PDF)
   
   
2. Sequences and Annotation (~190M) used in
   X. Mei, H. Ling, Y. Wu, E. Blasch, and L. Bai, Minimum Error Bounded Efficient L1 Tracker with Occlusion Detection, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2011. (PDF)
   
   
3. BLUT tracker (~11M), the L1 based tracker for tracking target through blurred sequence.
   Y. Wu, H. Ling, J. Yu, F. Li, X. Mei, and E. Cheng. "Blurred Target Tracking by Blur-driven Tracker". IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain, 2011. (PDF, ~11M)
   
   
4. TUBlur sequences and annotation (~190M), blurred videos annotation used in
   Y. Wu, H. Ling, J. Yu, F. Li, X. Mei, and E. Cheng. "Blurred Target Tracking by Blur-driven Tracker". IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain, 2011. (PDF, ~11M)

1. DT-OC5, DT-OC5.zip
2. Category-16 OC Dataset, category-16.zip
   Jin Sun, Christopher Thorpe, Nianhua Xie, Jingyi Yu, and Haibin Ling. Category Classification Using Occluding Contours, Int. Symposium on Visual Computing (ISVC), 2010.

1. BRD-dissimilarities
   Matlab code for dissimilarities used in the following paper
   N. Xie, H. Ling, W. Hu, and X. Zhang. Use Bin-Ratio Information for Category and Scene Classification, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), San Francisco, 2010.

Robust histogram comparison

1. EMD_L1, Efficient Earth Mover's Distance with L1 Ground Distance
   C++ Code
   R warpper provided by Rainer M Krug and Dirk Eddelbuettel
   H. Ling and K. Okada, An Efficient Earth Mover's Distance Algorithm for Robust Histogram Comparison, IEEE Trans on Pattern Anal. and Mach. Intell. (PAMI), 29(5):840-853, 2007
   
   
2. Diffusion-based distance
   Code | Dataset 1 | Dataset 2
   H. Ling and K. Okada, Diffusion Distance for Histogram Comparison, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. I, pp. 246-253, 2006.
   
   

Shape Matching

1. Inner-distance Shape Context
   Matching (~113K) | Experiment on Articulated Shapes
   Experiment on the Swedish leaves (~1M) | Experiment on the MPEG7 shapes (~1.9M)
   Smithsonian Leaves (4.5M)
   H. Ling and D.W. Jacobs, Shape Classification Using the Inner-Distance, IEEE Trans on Pattern Anal. and Mach. Intell. (PAMI), 29(2):286-299, 2007
   
   

Deformation Invariant Matching

1. Geodesic Instensity Histogram (GIH)
   Matlab demo code (~900K) | Dataset 1, Dataset 2 |
   H. Ling and D.W. Jacobs, Deformation Invariant Image Matching, IEEE International Conference on Computer Vision (ICCV) Vol. II, pp. 1466-1473, 2005.
   
   

Image Summarization

1. Scale and Object Aware Image Cropping (SOAT-CR)
   Matlab code (~225K)
   Dataset and our results (~45M)
   J. Sun and H. Ling, Scale and Object Aware Image Thumbnailing, International Journal of Computer Vision (IJCV), 2013.
   
   
2. Automatic Thumnail Cropping
   Matlab code (~58K)
   B. Suh, H. Ling, B.B. Bederson, and D.W. Jacobs, Automatic Thumbnail Cropping and Its Effectiveness, ACM Symposium on User Interface Software and Technology (UIST), CHI Letters, 5(2), pp. 95-104, 2003.