# ICCV 2005 Beijing, Short Course, Oct 15

Model: Parts and Structure History of Idea Fischler & Elschlager 1973 Yuille 91 Brunelli & Poggio 93 Lades, v.d. Malsburg et al. 93 Cootes, Lanitis, Taylor et al. 95 Amit & Geman 95, 99 Perona et al. 95, 96, 98, 00, Huttenlocher et al. 00

Many papers since 2000 Representation Object as set of parts Generative representation Model:

Relative locations between parts Appearance of part Issues: How to model location How to represent appearance Sparse or dense (pixels or regions) How to handle occlusion/clutter Figure from [Fischler73] Example scheme Model shape using Gaussian distribution on location between parts Model appearance as pixel templates

Represent image as collection of regions Extracted by template matching: normalized-cross correlation Manually trained model Click on training images

Sparse representation + Computationally tractable (105 pixels 101 -- 102 parts) + Generative representation of class + Avoid modeling global variability + Success in specific object recognition - Throw away most image information - Parts need to be distinctive to separate from other classes The correspondence problem Model with P parts Image with N possible locations for each part NP combinations!!!

Connectivity of parts Complexity is given by size of maximal clique in graph Consider a 3 part model Each part has set of N possible locations in image Location of parts 2 & 3 is independent, given location of L Each part has an appearance term, independent between parts. Shape Model L 2 3 Different graph structures 6

1 2 3 4 2 Fully connected O(N6) 2

1 5 6 3 4 5 Star structure 3

5 4 6 1 Tree structure O(N2) Sparser graphs cannot capture all interactions between parts O(N2)

Some class-specific graphs Articulated motion People Animals Special parameterisations Limb angles Images from [Kumar05, Feltzenswalb05] Regions or pixels # Regions << # Pixels Regions increase tractability but lose information Generally use regions: Local maxima of interest

operators Can give scale/orientation invariance Figures from [Kadir04] How to model location? Explicit: Probability density functions Implicit: Voting scheme Invariance Translation Scaling Similarity/affine Viewpoint Similarity

transformation Translation AffineTranslation transformation and Scaling Explicit shape model Probability densities Continuous (Gaussians) Analogy with springs Parameters of model, and Independence corresponds to zeros in Shape Shape is what remains after differences due to translation,

rotation, and scale have been factored out. [Kendall84] Y X Figure Space x1 M x X = N y1 M y N

V Shape Space U u3 M x U = N v3 M

v N Statistical theory of shape [Kendall, Bookstein, Mardia & Dryden] Figures from [Leung98] Representation of appearance Dependencies between parts Common to assume independence Need not be Symmetry Needs to handle intra-class variation Task is no longer matching of descriptors

Implicit variation (VQ appearance) Explicit probabilistic model of appearance (e.g. Gaussians in SIFT space or PCA space) Representation of appearance Invariance needs to match that of shape model Insensitive to small shifts in translation/scale Compensate for jitter of features e.g. SIFT Illumination invariance Normalize out Condition on illumination of

landmark part Parts and Structure demo Gaussian location model star configuration Translation invariant only Use 1st part as landmark Appearance model is template matching Manual training User identifies correspondence on training images Recognition

Run template for each part over image Get local maxima set of possible locations for each part Impose shape model - O(N2P) cost Score of each match is combination of shape model and template responses. Demo images Sub-set of Caltech face dataset Caltech background images Learning using EM Task: Estimation of model parameters

Chicken and Egg type problem, since we initially know neither: - Model parameters - Assignment of regions to parts Let the assignments be a hidden variable and use EM algorithm to learn them and the model parameters Example scheme, using EM for maximum likelihood learning 1. Current estimate of 2. Assign probabilities to constellations Large P ... pdf

Image 2 Image 1 Image i Small P 3. Use probabilities as weights to re-estimate parameters. Example: Large P x + Small P

x + = new estimate of Learning Shape & Appearance simultaneously Fergus et al. 03

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