# Blind city classification using aggregation of clusterings

Τεχνίτη Νοημοσύνη και Ρομποτική

25 Νοε 2013 (πριν από 4 χρόνια και 5 μήνες)

71 εμφανίσεις

Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

1

Blind city classification using aggregation
of clusterings

Ivan Kyrgyzov, Henri Maître

T
él
écom

Paris

7
th

CNES/DLR Workshop on Information Extraction and Scene
Understanding for Meter Resolution Images

Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

2

Plan of presentation

1.
Problem statement

2.
Unsupervised clustering algorithms

3.
The optimal number of clusters (MDL criterion)

4.
Usupervised clustering combination

5.
General schema of image clustering

6.
Image data

7.
Results

Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

3

1. Problem statement

Satellite Image Analysis as a Pattern Recognition Problem

Questions
:

Problem
: different algorithms give different clusterings

different views of data representations

some clusters are common, some
clusters
are unique

Method
:
unsupervised
clustering algorithms

1) Do not search classes manually.

2) Descriptors form clusters.

Solution
: combine results of clustering algorithms

and find a consensus

Which patterns?

How many pattern prototypes?

How to discriminate them?

Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

4

2. Unsupervised

clustering algorithms

EM
-
algorithm for GMM estimation (Autoclass, Cheeseman, 1996)

Kernel K
-
means algorithm (
Taylor & Cristianini 2004)

Spectral K
-
means algorithm (
A.Ng, 2002
)

Ward hierarchical algorithm (Ward, 1963)

Combining different

clusterings

using

a

co
-
association

matrix

Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

5

3.
The optimal number of clusters (MDL)

Minimum Description Length (Rissanen, 1978)

)
log(
2
1
))
|
(
log(
min
,
M
P

(1)

(2)

P(X|
Θ
)

of Gaussians and a “hard clustering” (1) is:

i
i
i
i
const
M
n
n
log
2
1
log

i
n
= number of samples in i
th

cluster,

i

= determinant of covariance matrix

i

calculated both for spectral and kernel clustering algorithms

Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

6

4. Unsupervised clustering combination

2
min
arg
T
XX
C
X

1
,
0
,

X
I
X
X
to
subject
T
Co
-
association

matrix

s
clustering
of
number
,
1
1
N
D
N
C
N
n
n

otherwise.
0,
;
clustering

n
for
cluster

same

in the

are

j

and

i

if

,
1
th
n
ij
D
Fred & Jain

(PAMI 2005)

Final

c
ombination

= , such that:

X
(3)

otherwise.
0,
Q;
1,...,
q

i,

q,

i

if

,
1

iq
X
Combination algorithm:

single
-
link based clustering (1974, Hubert) to minimize (3)

Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

7

5. General schema of image clustering

SPOT5
images

Samples

Image data

Models

Model 1

Model 2

Selected
Models

SModel 1

SModel 2

Clustering 1

Clustering 2

Sample
extraction

Feature
extraction

Unsupervised

feature

selection

Unsupervised

clustering

Unsupervised

combination

2
min
T
XX
C

Consensus

clustering

Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

8

6. Image data SPOT5, 5 m/pixel

(64x64 pixels)

529 samples per image

Sample extraction

CNES

Original images

Database of samples

Sample features

Haralick

Geometrical

QMF

Gabor

Sample 1

. . .

. . .

. . .

. . .

Sample 2

. . .

. . .

. . .

. . .

Feature extraction

and selection

Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

9

6.1. Image data SPOT5, 5 m/pixel

Barcelona

Los Angeles

Istanbul

Image examples of world cities

Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

10

7. Results. Clustering by Autoclass

Image examples of world cities

Estimated number of clusters: 14

Barcelona

Los Angeles

Istanbul

Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

11

7.1. Clustering by Kernel K
-
means

Optimal number of clusters: 7

2
4
6
7
8
10
12
14
16
-5.05
-5
-4.95
-4.9
-4.85
-4.8
-4.75
-4.7
-4.65
-4.6
x 10
4
The number of clusters
MDL criterion
Kernel K-means
Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

12

7.2. Clustering by Kernel K
-
means

Image examples of world cities

Optimal number of clusters: 7

Barcelona

Los Angeles

Istanbul

Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

13

7.3. Clustering by Spectral K
-
means

Optimal number of clusters: 9

2
4
6
8
9
10
12
14
16
18
20
-2.2
-2
-1.8
-1.6
-1.4
-1.2
-1
-0.8
x 10
4
The number of clusters
MDL criterion
Spectral K-means
Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

14

7.4. Clustering by Spectral K
-
means

Image examples of world cities

Optimal number of clusters: 9

Barcelona

Los Angeles

Istanbul

Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

15

7.5. Clustering by Ward algorithm

Optimal number of clusters: 4

2
4
6
8
10
12
14
16
-3.4
-3.3
-3.2
-3.1
-3
-2.9
-2.8
x 10
4
The number of clusters
MDL criterion
Ward clustering
Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

16

7.6.
Clustering by Ward algorithm

Image examples of world cities

Optimal number of clusters: 4

Barcelona

Los Angeles

Istanbul

Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

17

7.7. Unsupervised combination

Optimal number of clusters: 12

0
5
10
12
15
20
25
30
35
40
10
5
10
6
10
7
Number of clusters
Min

2
log
T
XX
C
Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

18

7.8. Unsupervised combination

Image examples of world cities

Optimal number of clusters: 12

Barcelona

Los Angeles

Istanbul

Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

19

Conclusions

consensus solution which reduces redundant
information in clusterings

relevant unsupervised approach

interpretable clusters and their relations

generalizes

information and helps to understand an
image context

Combination of different optimal clusterings:

Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

20

Perspectives

-
Unsupervised combination can be applied to data with different

clustering algorithms and metrcis, groups of
features,

classifications, segmentations,
maps,
labellings,

temporal images, etc., …

-

Using clusters for semi
-

Construction of a satellite image semantic

Propose to the user images from different clusters which will be

associated with semantic terms by human interpretation.

Competence Centre on Information Extraction

and Image Understanding for Earth Observation

29/03/07

Blind city classification using aggregation of clusterings

21