MIT 6.899
Learning and Inference in Vision
•
Prof. Bill Freeman, wtf@mit.edu
•
MW 2:30
–
4:00
•
Room: 34

301
•
Course web page:
http://www.ai.mit.edu/courses/6.899/
Reading class
•
We’ll cover about 1 paper each class.
•
Seminal or topical research papers in the
intersection of machine learning and vision.
•
One student will present each paper. Then
we’ll discuss the paper as a class.
•
One student will write a computer example
illustrating the paper’s main idea.
Learning and Inference
•
“Learning”: learn the parameter values or
structure of a probabilistic model.
–
Look at many examples of people walking, and
build up probabilistic model relating video
images to 3

d motions.
•
“Inference”: infer hidden variables, given a
observations.
–
Eg, given a particular video of someone
walking, infer their motions in 3

d.
Statistical
dependencies
between variables
Learning and Inference
y1
y2
Observed variables
x1
x2
Unobserved variables
Statistical
dependencies
between variables
Learning and Inference
Observed variables
Unobserved variables
“
Learning
”: learn this model, and the form
of the statistical dependencies.
Statistical
dependencies
between variables
Learning and Inference
y1
y2
Observed variables
x1
x2
Unobserved variables
“Learning”: learn this model, and the form
of the statistical dependencies.
“
Inference
”: given this model, and the
observations, y1 & y2, infer x1 & x2, or
their conditional distribution.
Cartoon history of speech
recognition research
•
1960’s, 1970’s, 1980’s: lots of different
approaches; “hey, let’s try this”.
•
1980’s Hidden Markov Models (HMM),
statistical approach took off.
•
1990’s and beyond: HMM’s now the
dominant approach. “The person with the
best training set wins”.
Same story for document
understanding
•
The person with the best training set wins.
Computer vision is ready to make
that transition
•
Machine learning approaches are becoming
dominant.
•
We get to make and watch the transition to
principled, statistical approach happen.
•
It’s not trivial: issues of representation,
robustness, generalization, speed, …
Categories of the papers
1.
Learning image representations
2.
Learning manifolds
3.
Linear and bilinear models
4.
Learning low

level vision
5.
Graphical models, belief propagation
6.
Particle filters and tracking
7.
Face and object recognition
8.
Learning models of object appearance
1 Learning image representations
Example training image
From http://www.amsci.org/amsci/articles/00articles/olshausencap1.html
1 Learning image representations
From: http://www.cns.nyu.edu/pub/eero/simoncelli01

reprint.pdf
2 Learning manifolds
From: http://www.sciencemag.org/cgi/content/full/290/5500/2319
Joshua B. Tenenbaum, Vin de Silva, John C. Langford
2 Learning manifolds
From: http://www.sciencemag.org/cgi/content/full/290/5500/2319
2 Learning manifolds
From: http://www.sciencemag.org/cgi/content/full/290/5500/2319
3 Linear and bilinear models
From: http://www

psych.stanford.edu/~jbt/NC120601.pdf
4 Learning low

level vision
From Y. Weiss, http://www.cs.berkeley.edu/~yweiss/iccv01.ps.gz
Images, under
different lighting
reflectance
illumination
5 Graphical models, belief propagation
From: http://www.cs.berkeley.edu/~yweiss/nips96.pdf
6 Particle filters and tracking
From: http://www.robots.ox.ac.uk/~ab/abstracts/eccv96.isard.html
7 Face and object recognition
From Viola and Jones, http://www.ai.mit.edu/people/viola/research/publications/ICCV01

Viola

Jones.ps.gz
7 Face and object recognition
From Viola and Jones, http://www.ai.mit.edu/people/viola/research/publications/ICCV01

Viola

Jones.ps.gz
7 Face and object recognition
From: Pinar Duygulu, Kobus Barnard, Nando deFreitas, and David Forsyth,
8 Learning models of object appearance
Weber, Welling, and Perona, http://www.gatsby.ucl.ac.uk/~welling/papers/ECCV00_fin.ps.gz
Images containing
the object
Images not containing
the object
8 Learning models of object appearance
Test images
Weber, Welling, and Perona, http://www.gatsby.ucl.ac.uk/~welling/papers/ECCV00_fin.ps.gz
Contains the
object?
Contains the
object?
8 Learning models of object appearance
Weber, Welling, and Perona, http://www.gatsby.ucl.ac.uk/~welling/papers/ECCV00_fin.ps.gz
Guest lecturers/discussants
•
Andrew Blake (Condensation,
Oxford/Microsoft)
•
Baback Moghaddam (Bayesian face
recognition, MERL)
•
Paul Viola (Fast face recognition, MERL)
Class requirements
1.
Read each paper. Think about them.
Discuss in class.
2.
Present one paper to the class.
3.
Present one computer example to the
class.
4.
Final project: write a conference paper
related to vision and learning.
1. Read the papers, discuss them
•
Write down 3 insights about the paper that
you might want to share with the class in
discussion.
•
Turn them in on a sheet of paper.
2. Presentations about a paper
•
About 15 minutes long. Set the stage for
discussions.
•
Review the paper. Summarize its
contributions. Give relevant background.
Discuss how it relates to other papers we’ve
read.
•
Meet with me two days before to go over
your presentation about the paper.
3. Programming example
•
Present a computer implementation of a toy
example that illustrates the main idea of the
paper.
•
Show trade

offs in parameter settings, or in
training sets.
•
Goal: help us build up intuition about these
techniques.
•
Ok to use on

line code. Then focus on
creating informative toy training sets.
Toy problems
•
Simple summaries of the main idea.
•
Identify an informative idea from the paper
•
Make a simple example using it.
•
Play with it.
Toy problem
by Ted Adelson
Toy problem
“If you can make a
system to solve this,
I’ll give you a PhD”
by Ted Adelson
Particle filter for inferring human
motion in 3

d
From: Hedvig Sidenbladh’s thesis, http://www.nada.kth.se/~hedvig/publications/thesis.pdf
Particle filter toy example
From: Hedvig Sidenbladh’s thesis, http://www.nada.kth.se/~hedvig/publications/thesis.pdf
What we’ll have at the end of the class
Non

negative matrix factorization example
1

d particle filtering example
Boosting for face recognition
Example of belief propagation for scene
understanding.
Manifold learning comparisons.
…
Code examples
4. Final project: write a conference paper
•
Submitting papers to conferences, you get just one
shot, so it’s important to learn how to make good
submissions.
•
We’ll discuss many papers, and what’s good and
bad about them, during the class.
•
I’ll give a lecture on “how to write a good
conference paper”.
•
Subject of the paper can be:
–
A project from your own research.
–
A project you undertake for the class.
•
Your idea
•
One I suggest to you
Feedback options
•
At the end of the course
: “it would have
been better if we had done this…”
–
Somewhat helpful
•
During the course
: “I find this useful; I
don’t find that useful…”
–
Very helpful
What background do you need?
•
Be able to read and understand the papers
–
Linear algebra
–
Familiarity with estimation theory
–
Image filtering
•
Background in machine learning and
computer vision.
Auditing versus credit
•
If you’re a student and want to take the
class, sign up for credit.
–
You’ll stay more engaged.
–
Makes it more probable that I can offer the
class again.
•
But if you do audit:
–
Please don’t come to class if you haven’t read
the paper.
–
I may ask you to present to the class, anyway.
First paper
•
Monday, Feb. 11.
•
Emergence of simple

cell receptive field properties
by learning a sparse code for natural images,
Olshausen BA, Field DJ (1996) Nature, 381: 607

609
•
Presenter: Bill Freeman
•
Computational demonstration: need volunteer
(software is available:
http://redwood.ucdavis.edu/bruno/sparsenet.html)
Second paper
•
Wednesday, Feb. 13.
•
Learning the parts of objects by non

negative
matrix factorization, D. D. Lee and H. S. Seung,
Nature 401, 788

791 (1999), and commentary
by Mel.
•
Presenter: need volunteer
•
Computational demonstration: need volunteer
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