1 michael bronstein shapes as metric spaces: deformation-invariant similarity michael bronstein...
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1Michael Bronstein Shapes as metric spaces: deformation-invariant similarity
Michael Bronstein
Computational metric geometry:an old new tool in image sciences
2Michael Bronstein Shapes as metric spaces: deformation-invariant similarity
3Michael Bronstein Shapes as metric spaces: deformation-invariant similarity
retrieval categorization tracking
detection/recognition restoration alignment
Similarity
4Michael Bronstein Shapes as metric spaces: deformation-invariant similarity
Raffaello Santi, School of Athens, Vatican
5Michael Bronstein Shapes as metric spaces: deformation-invariant similarity
Shape similarity and correspondence
Metric space Metric space
Correspondence
Correspondence quality = metric distortion
Similarity
Gromov-Hausdorff distance = Minimum possible correspondence distortion
6Michael Bronstein Shapes as metric spaces: deformation-invariant similarity
Invariance
Rigid Inelastic TopologyScale Elastic
Choice of the metric prescribes the invariance
7Michael Bronstein Shapes as metric spaces: deformation-invariant similarity
Non-rigid shape analysis and synthesis
BBK
Correspondence Morphing
Retrieval
8Michael Bronstein Shapes as metric spaces: deformation-invariant similarity
Self-similarity and symmetry
Permutation
Raviv & BBK 2007
9Michael Bronstein Shapes as metric spaces: deformation-invariant similarity
Metric learning
Data space Embedding space
Min distortion on training set of examples with known
10Michael Bronstein Shapes as metric spaces: deformation-invariant similarity
Video copy detection
Luke vs Vader – Starwars classic
Lightsaber
Star Wars DVD copy Star Wars pirated copy
BBK 2010
11Michael Bronstein Shapes as metric spaces: deformation-invariant similarity
Challenges
Theoretical
•Approximate symmetry notion: group-like structure
•Comparing data from different spaces
Computational
•Efficient solution of minimum distortion correspondence problems
(Gromov-Hausdorff distance)
•Efficient algorithms for embedding into interesting metric spaces
Applications
•Problems that can be formulated in terms of metric geometry