Sparse CodingArthur Pece

aecp@cs.rug.nl

Outline

Generative-model-based vision Linear, non-Gaussian, over-complete generative models The penalty method of

Olshausen+Field & Harpur+Prager Matching pursuit The inhibition method An application to medical images A hypothesis about the brain

Generative-Model-Based Vision

Generative models Bayes’ theorem (gives an objective function) Iterative optimization (for parameter estimation) Occam’s razor (for model selection)

A less-fuzzy definition of model-based vision.Four basic principles (suggested by the speaker):

Why?

A generative model and Bayes’ theorem lead to a better understanding of what the algorithm is doing

When the MAP solution cannot be found analytically, iterating between top-down and bottom-up becomes necessary (as in EM, Newton-like and conjugate-gradient methods)

Models should not only be likely, but also lead to precise predictions, hence (one interpretation of) Occam’s razor

Linear Generative Models

x is the observation vector (n samples/pixels) s is the source vector (m sources) A is the mixing matrix (n x m) n is the noise vector (n dimensions)

The noise vector is really a part of the source vector

x = A.s + n

Learning vs. Search/Perception

Learning: given an ensemble X = {x i},

maximize the posterior probability of the

mixing matrix A Perception: given an instance x,

maximize the posterior probability of the

source vector s

x = A.s + n

MAP Estimation

From Bayes’ theorem:

log p(A | X) = log p(X | A) + log p(A) - log p(X)

(marginalize over S)

log p(s | x) = log p(x | s) + log p(s) - log p(x)

(marginalize over A)

Statistical independence of the sources

Why is A not the identity matrix ? Why is p(s) super-Gaussian (lepto-kurtic)? Why m>n ?

Why is A not the identity matrix?

Pixels are not statistically independent:

log p(x) /= Σ log p(x i)

Sources are (or should be) statistically independent:

log p(s) = Σ log p(s j)

Thus, the log p.d.f. of images is equal to the sum of the log p.d.f.’s of the coefficients,NOT equal to the sum of the log p.d.f.’s of the pixels.

Why is A not the identity matrix?(continued)

From the previous slide:

Σ log p(c j) /= Σ log p(x i)

But, statistically, the estimated probability of an image is higher if the estimate is given by the sum of coefficient probabilities, rather than the sum of pixel probabilities:

E [ Σ log p(c j) ] > E [ Σ log p(x i) ]This is equivalent to:

H [ p(c j)] < H [ p(x i)]

Why m>n ?

Why is p(s) super-Gaussian?The image “sources” are edges; ultimately, objects Edges can be found at any image location and can

have any orientation and intensity profile Objects can be found at any location in the scene

and can have many different shapes Many more (potential) edges or (potential) objects

than pixels Most of these potential edges or objects are not

found in a specific image

Linear non-Gaussian generative model

Super-Gaussian prior p.d.f. of sources Gaussian prior p.d.f. of noise

log p(s | x,A) = log p(x | s,A) + log p(s) – log p(x | A)

log p(x | s,A) = log p(x - A.s) = log p(n)= - n.n/(2σ2) - log Z= - || x - A.s ||2/(2σ2) - log Z

Linear non-Gaussian generative model (continued)

Example: Laplacian p.d.f. of sources:log p(s) = - Σ |s| / λ - log Q

log p(s | x,A) = log p(x | s,A) + log p(s) – log p(x | A) = - || x - A.s ||2 /(2σ2)

- Σ |s| / λ - const.

Summary

Generative-model-based vision Learning vs. Perception Over-complete expansions Sparse prior distribution of sources Linear over-complete generative model with

Laplacian prior distribution for the sources

The Penalty Method: Coding

Gradient-based optimization of the log-posterior probability of the coefficients

(d/ds) log p(s | x) = - AT .(x - A.s) / σ2 - sign(s) / λ

Note: as the noise variance tends to zero, the quadratic term dominates the right-hand sideand the MAP estimate could be obtained by solvinga linear system.However, if m>n then minimizing a quadratic objective function would spread the image energy over non-orthogonal coefficients

Linear inferencefrom linear generative modelswith Gaussian prior p.d.f.

The logarithm of a multivariate Gaussian is a weighted sum of squares

The gradient of a sum of squares is a linear function

The MAP solution is the solution of a linear system

Non-linear inferencefrom linear generative modelswith non-Gaussian prior p.d.f.

The logarithm of a multivariate non-Gaussian p.d.f. is NOT a weighted sum of squares

The gradient of a non-Gaussian p.d.f. is NOT a linear function

The MAP solution is NOT the solution of a linear system: in general, no analytical solution exists (this is why over-complete bases are not popular)

PCA, ICA, SCA

PCA generative model: multivariate Gaussian

-> closed-form solution ICA generative model: non-Gaussian

-> iterative optimization over image ensemble SCA generative model:

over-complete non-Gaussian

-> iterate for each image for perception, over the image ensemble for learning

The Penalty Method: Learning

Gradient-based optimization of the log-posterior probability* of the mixing matrix

Δ A = - A (z . cT + I)

where

z j = (d/dsj ) log p(sj )

and c is the MAP estimate of s

* actually log-likelihood

Summary

Generative-model-based vision Learning vs. Perception Over-complete expansions Sparse prior distribution of sources Linear over-complete generative model with

Laplacian prior distribution for the sources Iterative coding as MAP estimation of sources Learning an over-complete expansion

Vector Quantization

General VQ: K-means clustering of signals/images

Shape-gain VQ: clustering on the unit sphere

(after a change from Cartesian to polar coordinates)

Iterated VQ: iterative VQ of the residual signal/image

Matching Pursuit

Iterative shape-gain vector quantization: Projection of the residual image onto all

expansion images Selection of the largest (in absolute value)

projection Updating of the corresponding coefficient Subtraction of the updated component from the

residual image

Inhibition Method

Similar iteration structure, but more than one coefficient updated per iteration:

Projection of the residual image onto all expansion images

Selection of the largest (in absolute value) k projections Selection of orthogonal elelemnts in this reduced set Updating of the corresponding coefficients Subtraction of the updated components from the

residual image

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MacKay Diagram

Selection inMatching Pursuit

Selection in the Inhibition Method

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Encoding natural images: Lena

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Encoding natural images: a landscape

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Encoding natural images: a bird

Comparison to the penalty method

Visual comparisons

JPEG inhibition method penalty method

Expanding the dictionary

An Application to Medical Images

X-ray images decomposed by means of matching pursuit

Image reconstruction by optimally re-weighting the components obtained by matching pursuit

Thresholding to detect micro-calcifications

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Tumor detection in mammograms

Residual image after several matching pursuit iterations

Image reconstructed from matching-pursuit components

Weighted reconstruction

Receiver Operating Curve

A Hypothesis about the Brain

Some facts All input to the cerebral cortex is relayed

through the thalamus: e.g. all visual input from the retina is relayed through the LGN

Connections between cortical areas and thalamic nuclei are always reciprocal

Feedback to the LGN seems to be negativeHypothesis: cortico-thalamic loops minimize

prediction error

Additional references

Donald MacKay (1956) D Field (1994) Harpur and Prager (1995) Lewicki and Olshausen (1999) Yoshida (1999)