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If A = C t C ,as C t is not full ranked (sum of rows is equal to zero), the
determinant of A is zero. Furthermore, let z = Cy ,thenhave y t C t Cy = z t z> =0
and hence A is positive semi-definite. The matrix A matrix is usually sparse for
large problems, with the diagonal elements a ij
= number of edges incidents to
vertex i ; and the off-diagonal elements:
a ij =
1 if an edge exists between vertices i and j
0otherwise
(2)
In the literature, this matrix is referred to as the Laplacian matrix (also called
topological or graph Laplacian), it plays a central role in various applications.
If
A =
,where . is the elementwise matrix multiplication operator (or
Hadamard-Schur product) and
|
A.Ψ
|
01
1
10 1
10 1
Ψ =
(3)
. . .
. . .
. . .
1
A is commonly known as the adjacency matrix of the graph.
2.2 N th Order Node-Edge Matrix
C as defined below represents the first order connectivity of the image. In many
cases, it might also be useful to define an extended node-edge matrix taking into
account ”weak” connection between nodes. For example, we might be interested
to define a ”weak” connection between second order neighbouring nodes. C can
be then defined by:
C 1
.
C N
C 1 ..N =
(4)
where C 1 represents the connection (between first order neighbours) and C N
represents the connection (between N t h order neighbours).
t
C 1
.
C N
C 1
.
C N
= C 1 C 1 +
+ C t N C N
A = C 1 ..N C 1 ..N =
.
···
(5)
Like in the definition of the node-edge matrix, the coecients of C k
are either
equals to 0, c k or
c k .The c k coecients are strictly positive and have to verify
the following properties:
1
c k
c k
(6)
c k +1
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