Learning Decompositional Learning Decompositional Shape Models from Shape Models from

ExamplesExamples

Alex LevinshteinAlex Levinshtein

Cristian SminchisescuCristian Sminchisescu

Sven DickinsonSven Dickinson

The Evolution of Object Recognition

Appearance-based modelsAppearance-based models

Automatically built appearance-based model from video sequence(Ramanan, D. and Forsyth, D.A., “Using Temporal Coherence to Build Models of Animals”, ICCV, 2003)

Constellation model(Fergus, R., Perona, P., and Zisserman, A., “Object Class Recognition by Unsupervised Scale-Invariant Learning”, CVPR, 2003)

Appearance-based modelsAppearance-based models

Hierarchical ModelsHierarchical ModelsManually built hierarchical model proposed by Marr And Nishihara(“Representation and recognition of the spatial organization of three dimensional shapes”, Proc. of Royal Soc. of London, 1978)

Our goalOur goal

Automatically construct a generic hierarchical shape model from exemplars

Challenges:

Cannot assume similar appearance among different exemplars

Generic features are highly ambiguous

Generic features may not be in one-to-one correspondence

Automatically constructed Automatically constructed Hierarchical ModelsHierarchical Models

Input:

Question: What is it?

Output:

Stages of the systemStages of the systemExemplar images

Extract Blob Graphs

Match Blob Graphs (many-

to-many)

Extract PartsExtract

Decomposition Relations

Extract Attachment Relations

Assemble Final Model

Blob graphs

Many-to-many correspondences

Model parts

Model decomposition relations

Model attachment relations

Blob Graph ConstructionBlob Graph ConstructionExemplar images

Extract Blob Graphs

Match Blob Graphs (many-

to-many)

Extract PartsExtract

Decomposition Relations

Extract Attachment Relations

Assemble Final Model

Blob graphs

Many-to-many correspondences

Model parts

Model decomposition relations

Model attachment relations

Blob Graph ConstructionBlob Graph Construction

Choose the largest connected component.On the Representation and Matching of Qualitative Shape at Multiple Scales

A. Shokoufandeh, S. Dickinson, C. Jonsson, L. Bretzner, and T. Lindeberg,ECCV 2002

Blob Graph ConstructionBlob Graph Construction

Connectivity measure: max{d1/major(A), d2/major(B)}

Perceptual grouping of blobs:

Blob Graph ConstructionBlob Graph ConstructionEdge weights between

connected blobs:

Edge weights between disconnected blobs are computed based on shortest path distances.

Edge weights are invariant to articulation.

Feature matchingFeature matchingExemplar images

Extract Blob Graphs

Match Blob Graphs (many-

to-many)

Extract PartsExtract

Decomposition Relations

Extract Attachment Relations

Assemble Final Model

Blob graphs

Many-to-many correspondences

Model parts

Model decomposition relations

Model attachment relations

Feature matchingFeature matchingOne-to-one matching. Rely on shape and context, not appearance!

?

Many-to-many matching

Feature embeddingFeature embedding

00 171171 202202 230230

171171 00 373373 400400

202202 373373 00 432432

230230 400400 432432 00

Spectral embedding

Matching using Earth Mover’s Matching using Earth Mover’s DistanceDistance

EMD under Transformation EMD under Transformation AlgorithmAlgorithm

Returning to our set of Returning to our set of inputsinputs

Many-to-many matching of every pair of exemplars.

Part ExtractionPart ExtractionExemplar images

Extract Blob Graphs

Match Blob Graphs (many-

to-many)

Extract PartsExtract

Decomposition Relations

Extract Attachment Relations

Assemble Final Model

Blob graphs

Many-to-many correspondences

Model parts

Model decomposition relations

Model attachment relations

Results of the part extraction Results of the part extraction stagestage

What is next?What is next?

Extracting attachment Extracting attachment relationsrelations

Exemplar images

Extract Blob Graphs

Match Blob Graphs (many-

to-many)

Extract PartsExtract

Decomposition Relations

Extract Attachment Relations

Assemble Final Model

Blob graphs

Many-to-many correspondences

Model parts

Model decomposition relations

Model attachment relations

Extracting attachment Extracting attachment relationsrelations

Right arm is typically connected to torso in exemplar images !

Extracting attachment Extracting attachment relationsrelations

Number of times blobs from the two clusters co-appeared in an image.

Number of times blobs drawn from the two clusters were attached

Extracting decomposition Extracting decomposition relationsrelations

Exemplar images

Extract Blob Graphs

Match Blob Graphs (many-

to-many)

Extract PartsExtract

Decomposition Relations

Extract Attachment Relations

Assemble Final Model

Blob graphs

Many-to-many correspondences

Model parts

Model decomposition relations

Model attachment relations

Extracting decomposition Extracting decomposition relationsrelations

Extracting decomposition Extracting decomposition relationsrelations

Sum of all flows between blobs in two clusters

Number of flows between blobs in two clusters

Finding decompositions Finding decompositions (example)(example)

Model construction stage Model construction stage summarysummary

Model Construction:

Clustering blobs into parts based on one-to-one matching results.

Recovering relations between parts based on individual matching and attachment results.

Assemble Final ModelAssemble Final ModelExemplar images

Extract Blob Graphs

Match Blob Graphs (many-

to-many)

Extract PartsExtract

Decomposition Relations

Extract Attachment Relations

Assemble Final Model

Blob graphs

Many-to-many correspondences

Model parts

Model decomposition relations

Model attachment relations

Experiments

List of system parametersList of system parametersParameterParameter DescriptionDescription

Perceptual Perceptual Grouping Grouping ThresholdThreshold

The threshold determining the pairwise The threshold determining the pairwise connectivity between blobs in the exemplar connectivity between blobs in the exemplar imagesimages

DD The dimensionality of the embedding space The dimensionality of the embedding space during matchingduring matching

Embedding Embedding dimensionality dimensionality during part during part extractionextraction

The dimensionality of the embedding space The dimensionality of the embedding space used for blob clustering.used for blob clustering.

KK The maximum number of parts in the modelThe maximum number of parts in the model

TTattachattach The threshold for accepting attachment The threshold for accepting attachment between parts in the final modelbetween parts in the final model

TTchildchild The threshold for considering a part to be a The threshold for considering a part to be a potential child of another partpotential child of another part

TTdecompdecomp The threshold for accepting a decompositionThe threshold for accepting a decomposition

ConclusionsConclusions General framework for constructing a General framework for constructing a

generic decompositional model from generic decompositional model from different exemplars with dissimilar different exemplars with dissimilar appearance.appearance.

Recovering decompositional relations Recovering decompositional relations requires solving the difficult many-to-requires solving the difficult many-to-many graph matching problem.many graph matching problem.

Preliminary results indicate good model Preliminary results indicate good model recovery from noisy features.recovery from noisy features.

Future workFuture work

Construct models for objects other than Construct models for objects other than humans.humans.

Provide scale invariance during matching.Provide scale invariance during matching.

Automatically learn perceptual grouping Automatically learn perceptual grouping relations from labeled examples.relations from labeled examples.

Develop indexing and matching framework Develop indexing and matching framework for decompositional models.for decompositional models.