Machine Learning in Computer
Vision
b
y
N. SEBE
Universit
y
o
f
Amsterdam,
The
N
etherlan
d
s
IRA COHEN
ASHUTOSH GARG
an
d
THOMAS S. HUANG
Universit
y
o
f
Illinois at Urbana-Champai
g
n,
H
P Research Labs, U.S.A.
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e Inc., U.S.A
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I
SBN-10 1-4020-3274-9 (HB) Springer Dordrecht, Berlin, Heidelberg, New York
I
SBN-10 1-4020-3275-7 (e-book) Springer Dordrecht, Berlin, Heidelberg, New York
I
SBN-13 978-1-4020-3274-5 (HB) Sprin
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er Dordrecht, Berlin, Heidelber
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, New York
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SBN-13 978-1-4020-3275-2 (e-book) Springer Dordrecht, Berlin, Heidelberg, New York
To m
y
parent
s
N
icu
To Mera
v
and Yonatan
I
ra
T
om
y
parent
s
A
sutosh
To my students
:
P
ast, present, an
df
uture
To
m
Contents
Foreword xi
Pre
f
ace x
iii
1
. INTR
O
D
UC
TI
O
N
1
1
Researc
h
Issues on Learn
i
ng
i
n Computer V
i
s
i
on 2
2 Overview of the Book
6
3C
ontributions 12
2. THEORY
:
PROBABILISTIC CLASSIFIERS 1
5
1
Introduction 15
2 Pre
li
m
i
nar
i
es an
d
Notat
i
ons 1
8
2
.1 Max
i
mum L
ik
e
lih
oo
dCl
ass
ifi
cat
i
on 1
8
2
.2 In
f
ormat
i
on T
h
eory 1
9
2
.3 Inequa
li
t
i
es 20
3 Bayes Optimal Error and Entropy 2
0
4 Anal
y
sis of Classification Error of Estimated (
M
i
s
matc
h
e
d
)
Di
str
ib
ut
i
on 2
7
4
.1 H
y
pothesis Testin
g
Framework 2
8
4
.2 Classification Framework 30
5
Densit
y
of Distributions 3
1
5
.1 Distributional Density 3
3
5
.2 Relating to Classification Error 3
7
6
Complex Probabilistic Models and Small Sample Effects 4
0
7
S
ummar
y41
