Multi-task Low-rank Affinity Pursuit Multi-task Low-rank Affinity Pursuit for Image Segmentationfor Image Segmentation

Bin Cheng, Guangcan Liu, Jingdong Wang, Zhongyang Huang, Shuicheng Yan

(ICCV’ 2011)

Presented by Han Hu, I-vision Lab

ACM MM 2011We have six papers and one demo accepted to ACM MM 2011, with one full paper on Mul ti-Semantic Image Annotation. [08/08/2011]ICCV 2011We have six papers accepted to ICCV 2011, with one ORAL presentation from SONG Zheng and NI Bing bing on "Learning Universal Multi-view Age Estimator by Video Contexts". [07/23/2011]AAAI 2011Two papers on fea ture selection and block-di ag o nal regularization are accepted to AAAI'11. [04/28/2011]AISTATS 2011One paper from Xi ao tong Yuan is ac cept ed to International Con fer ence on Ar ti fi cial In tel li gence and Statis tics (AIS TATS). [03/07/2011]CVPR 2011Four papers are accepted to IEEE Con fer ence on Computer Vision and Pattern Recog ni tion (CVPR) 2011, including one oral pre sen ta tion. [03/07/2011]

Activity and event detection in images and videos Subspace learning and manifold learningTransductive learning, Transfer Learning Generic/Specific object detection, recognition and categorization Biometrics, Medical image processing

Shuicheng Yan

The Goal and MotivationThe Goal and Motivation

Related WorksRelated WorksNormalized Cut (PAMI’00)Multi-view Spectral Clustering (ICML’07)

◦Create Independent Similarity Graphs◦Fuse Different Graphs

The proposed MethodThe proposed MethodCreate Consistent Graphs

The proposed MethodThe proposed MethodCreate Consistent Graphs

SuperpixelsSuperpixels

The proposed MethodThe proposed MethodCreate Consistent Graphs

Compute K Feature MatricesCompute K Feature MatricesColor HistogramLBPSIFT-BOW

The proposed MethodThe proposed MethodCreate Consistent Graphs

Construct Similarity Graphs (1/2)Construct Similarity Graphs (1/2)

Single-Feature Case

Similarity：

[G. Liu et al, ICML’10]

Construct Similarity Graphs (2/2)Construct Similarity Graphs (2/2)

Multi-Feature Case

Similarity：

L_2,1 NormL_2,1 Norm

Optimization (Augmented Lagrange Optimization (Augmented Lagrange Multiplier)Multiplier)

OptimizationOptimization

EvaluationEvaluationDatasets

◦MSRC (591 images)◦Berkeley (500 images)

Results

Qualitative ResultsQualitative Results

Qualitative ResultsQualitative Results

SummarySummaryClustering by Low Rank RepresentationL_2,1 NormA way to do fusion