Object Segmentation in Video: A Hierarchical Object Segmentation in Video: A Hierarchical Variational Approach for Turning Point Variational Approach for Turning Point
Trajectories into Dense RegionsTrajectories into Dense Regions层次化变分法用于稠密的视频运动分割
Peter Ochs and Thomas BroxUniversity of Freiburg, Germany
ICCV 2011
Thomas Brox, Professor in University of Freiburg, Germany
Experience:
Received PhD from Saarland University in 2005.
2005-2007 Post Doctor in Born University.
2007-2008 Temporary Professor in University of Dresden.
2008-2010 Post Doctor in UC Berkerley with J. Malik.
Main Interests:Optical Flow, Segmentation, Human Motion
Representative Work:Brox Optical Flow(ECCV’04 best paper)
LDOF (PAMI’10)
Segmentation (ECCV’10)
Video SegmentationVideo SegmentationTwo Tasks
◦Shots Segmentation◦Spatial-Temperal Cues Segmentation
Motion Segmentation
Motion SegmentationMotion SegmentationOptical flow based
◦Earlier Methods◦Layers
Feature trajectory based◦Most Popular in the last 10 years◦Utilize 3D Motion◦Related to Subspace Clustering
Hybrid methods using both motion and static cues
Sparse Point Segmentation (1/6)Sparse Point Segmentation (1/6)
Thomas Brox and Jitendra Malik, Object Segmentation by Long Term Analysis of Point Trajectories, ECCV 2010
Optical Flow to obtain long-term point Trajectories
Sparse Point Segmentation (2/6)Sparse Point Segmentation (2/6)
Similarity Definition
Sparse Point Segmentation (3/6)Sparse Point Segmentation (3/6)
Similarity Definition
Sparse Point Segmentation (4/6)Sparse Point Segmentation (4/6)
Standard Spectral Clustering
Sparse Point Segmentation (5/6)Sparse Point Segmentation (5/6)
Spectral Clustering with Spatial Regularity◦Automatically determine cluster number
Sparse Point Segmentation (6/6)Sparse Point Segmentation (6/6)
Main Contribution◦Very Sparse Feature Points 0.01%-> Sparse
points (3%)
MotivationMotivation
Single-Level Variational modelSingle-Level Variational model
+
Solution (1/2)Solution (1/2)
Euler-Lagrange Equation:
Solution (2/2)Solution (2/2)Euler-Lagrange Equation:
Successive over-relaxation:
solve
by
AX=B, where A = D - L - U
Multi-Level Variational modelMulti-Level Variational model
Multi-level Variational ModelMulti-level Variational Model
Why Multi-level continuous model?Why Multi-level continuous model?
Multi-level◦Information can take a shortcut via a coarser
level where this noise has been removed.Continuous
◦Less block artifacts
SolutionSolution
Euler-Lagrange Equation:
k=0
k>0
SolutionSolution
Qualitative ResultsQualitative Results
Qualitative ResultsQualitative Results
Quantitative ResultsQuantitative Results
SummarySummaryCombining Motion Cues and Static CuesPropose a Multi-level Variational Method
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