A Julia set , a fractal related to the Mandelbrot set
A fractal that models the surface of a mountain (animation)
In mathematics , the Hutchinson metric otherwise known as Kantorovich metric is a function which measures "the discrepancy between two images for use in fractal image processing " and "can also be applied to describe the similarity between DNA sequences expressed as real or complex genomic signals".[ 1] [ 2]
Consider only nonempty , compact , and finite metric spaces . For such a space
X
{\displaystyle X}
, let
P
(
X
)
{\displaystyle P(X)}
denote the space of Borel probability measures on
X
{\displaystyle X}
, with
δ δ -->
:
X
→ → -->
P
(
X
)
{\displaystyle \delta :X\rightarrow P(X)}
the embedding associating to
x
∈ ∈ -->
X
{\displaystyle x\in X}
the point measure
δ δ -->
x
{\displaystyle \delta _{x}}
. The support
|
μ μ -->
|
{\displaystyle |\mu |}
of a measure in
P
(
X
)
{\displaystyle P(X)}
is the smallest closed subset of measure 1.
If
f
:
X
1
→ → -->
X
2
{\displaystyle f:X_{1}\rightarrow X_{2}}
is Borel measurable then the induced map
f
∗ ∗ -->
:
P
(
X
1
)
→ → -->
P
(
X
2
)
{\displaystyle f_{*}:P(X_{1})\rightarrow P(X_{2})}
associates to
μ μ -->
{\displaystyle \mu }
the measure
f
∗ ∗ -->
(
μ μ -->
)
{\displaystyle f_{*}(\mu )}
defined by
f
∗ ∗ -->
(
μ μ -->
)
(
B
)
=
μ μ -->
(
f
− − -->
1
(
B
)
)
{\displaystyle f_{*}(\mu )(B)=\mu (f^{-1}(B))}
for all
B
{\displaystyle B}
Borel in
X
2
{\displaystyle X_{2}}
.
Then the Hutchinson metric is given by
d
(
μ μ -->
1
,
μ μ -->
2
)
=
sup
{
∫ ∫ -->
u
(
x
)
μ μ -->
1
(
d
x
)
− − -->
∫ ∫ -->
u
(
x
)
μ μ -->
2
(
d
x
)
}
{\displaystyle d(\mu _{1},\mu _{2})=\sup \left\lbrace \int u(x)\,\mu _{1}(dx)-\int u(x)\,\mu _{2}(dx)\right\rbrace }
where the
sup
{\displaystyle \sup }
is taken over all real -valued functions
u
{\displaystyle u}
with Lipschitz constant
≤ ≤ -->
1.
{\displaystyle \leq \!1.}
Then
δ δ -->
{\displaystyle \delta }
is an isometric embedding of
X
{\displaystyle X}
into
P
(
X
)
{\displaystyle P(X)}
, and if
f
:
X
1
→ → -->
X
2
{\displaystyle f:X_{1}\rightarrow X_{2}}
is Lipschitz then
f
∗ ∗ -->
:
P
(
X
1
)
→ → -->
P
(
X
2
)
{\displaystyle f_{*}:P(X_{1})\rightarrow P(X_{2})}
is Lipschitz with the same Lipschitz constant.[ 3]
See also
Sources and notes