1. Multi-task Low-rank Affinity Pursuit for Image Segmentation Bin Cheng, Guangcan Liu, Jingdong Wang, Zhongyang Huang, Shuicheng Yan (ICCV’ 2011) Presented by Han Hu, I-vision Lab

2. ACM MM 2011 We have six papers and one demo accepted to ACM MM 2011, with one full paper on Mul­ti- Semantic Image Annotation. [08/08/2011] ICCV 2011 We have six papers accepted to ICCV 2011, with one ORAL presentation from SONG Zheng and NI Bing­bing on "Learning Universal Multi-view Age Estimator by Video Contexts". [07/23/2011]Learning Universal Multi-view Age Estimator by Video Contexts AAAI 2011 Two papers on fea­ture selection and block-di­ag­o­ nal regularization are accepted to AAAI'11. [04/28/2011] AISTATS 2011 One paper from Xi­ao­tong Yuan is ac­cept­ed to International Con­fer­ence on Ar­ti­fi­cial In­tel­li­gence and Statis­tics (AIS­TATS). [03/07/2011] CVPR 2011 Four papers are accepted to IEEE Con­fer­ence on Computer Vision and Pattern Recog­ni­tion (CVPR) 2011, including one oral pre­sen­ta­tion. [03/07/2011]  Activity and event detection in images and videos  Subspace learning and manifold learning  Transductive learning, Transfer Learning  Generic/Specific object detection, recognition and categorization  Biometrics, Medical image processing Shuicheng Yan

3. The Goal and Motivation

4. Related Works Normalized Cut (PAMI’00) Multi-view Spectral Clustering (ICML’07) ◦Create Independent Similarity Graphs ◦Fuse Different Graphs

5. The proposed Method Create Consistent Graphs

6. The proposed Method Create Consistent Graphs

7. Superpixels

8. The proposed Method Create Consistent Graphs

9. Compute K Feature Matrices Color Histogram LBP SIFT-BOW

10. The proposed Method Create Consistent Graphs

11. Construct Similarity Graphs (1/2) Single-Feature Case Similarity ： [G. Liu et al, ICML’10]

12. Construct Similarity Graphs (2/2) Multi-Feature Case Similarity ：

13. L_2,1 Norm

14. Optimization (Augmented Lagrange Multiplier)

15. Optimization

16. Evaluation Datasets ◦MSRC (591 images) ◦Berkeley (500 images) Results

17. Qualitative Results

19. Summary Clustering by Low Rank Representation L_2,1 Norm A way to do fusion