Texture Learning by Fractal Compression

Benoît Dolez

1,2

and Nicole Vincent

CRIP5-SIP, René Descartes University, 45 rue des Saints Pères

75006 Paris, France

SAGEM DS, 178 rue de Paris, 91344 Massy, France

Abstract. This paper proposes a texture learning method based on fractal com-

pression. This type of compression is efficient for extracting self-similarities in

an image. Square blocks are studied and similarities between them highlighted.

This allows establishing a score and thus a rating of the blocks. Selecting the

blocks that best encode the biggest part of the image or a texture leads to a da-

tabase of the most representative ones. The recognition step consists in labeling

blocks and pixels of a new image. The blocks of the new image are matched

with the ones of the different texture databases. As an application, we used our

method to recognize vegetation and buildings on aerial images.

1 Introduction

Learning a set of concepts and labeling areas of a new image is a fundamental issue in

the field of image processing. Texture analysis usually follows one of these ap-

proaches: structural [1], statistical [2], model-based [3] [4], or transform [5]. Low-

level features rarely well classify complex concepts. For example, a building contains

homogenous and geometric areas. Our aim is to take this composite aspect into ac-

count by extracting the most representative blocks of the concepts samples. The

means we chose is the fractal compression. Fractal theory has been widely worked

out during the last two decades ([6], [7], [8], [9], [10]). The basis of this kind of com-

pression is the search for similarities in the image. A self-similarity corresponds to a

redundancy of information in the signal, expect for scale, contrast, luminosity or

isometry. The recognition step will be equivalent to searching the learnt blocks in the

test image. This type of approach was explored in [11] for handwriting analysis. This

paper proposes to continue in this way and extend the previous study to grey scale

images and texture learning.

Dolez B. and Vincent N. (2007).

Texture Learning by Fractal Compression.

In Proceedings of the 7th International Workshop on Pattern Recognition in Information Systems, pages 99-106

DOI: 10.5220/0002429200990106

 SciTePress

2 Fractal Compression

2.1 Global Principle

The fractal compression relies on Iterated Function Systems (IFS). An IFS is a set of

contracting functions

MMT

→: in a metric space

. This contracting function

can be extended to the set of parts of

and considering the Hausdorff distance.

() ()

MPMPTT

→=

(1)

The fix point theorem gives the existence and uniqueness of a subset

F of

that

FFT =)( . F is called the attractor of the IFS. In the fractal compression

field, it rarely occurs that an image is really self-similar. For this reason, we use the

principle of Partitioned IFS (PIFS): the image is partitioned, then, for each element of

the partition we aim at finding an area of the image which should correspond, apart

from a contracting transform. In our case the partition is a regular grid. Its elements

are called ranges. We have chosen them square. The zones of the image that may

correspond are called domains.

The decompression step of the image is limited to the application of the PIFS. The

initial point is an image that has the same dimension as the compressed image but any

content is convenient (average grey image for example or an ordinary image). At each

iteration, the image is transformed and converges towards the image compressed by

the PIFS. We stop the iterated process when the reconstructed image is good enough

or when there is no variation anymore from one iteration to the other. Many papers

have been published in this field during the last two decades. Some of them deal more

specially with the memory size required to encode the compressed image [12]. Others

study the optimization of correspondence search between similar elements in the

image ([10], [13], [14], [15], [16], [17]).

2.2 Contracting Transform Construction

To ensure contracting property, the magnitude of the scale factor between a domain

and a range, and the contrast parameter must be lesser than one. Finding the contract-

ing transform is equivalent to find, for each couple of range

and domain D , the

parameters that best match the following expression:

(

)

(

)

lciIsosDresisoR

≅ .,, .

(2)

where

()

sDred , is the result of scaling D by a s factor,

(

)

iIsoBiso , is the ap-

plication of an isometry referred by

iIso on image block

. c and l are contrast

and luminance parameters and can be computed by least square method. To respect

the square shape of the blocks we limit the considered isometries to the eight possi-

bilities of rotations and symmetries (

[

]

8..1

∈

iIso ). In practice, the scale factor can

100

be fixed to

as explained by Jacquin in [7]. We say that domain D encodes range

if the matching between D and

, by

(

)

lcsiIsoRDT ,,,,,

, is good enough,

that is to say they can be considered as similar according to some given criterion:

PSNR, RMS, etc.

3 Learning Process

3.1 Domain Score Definition

The compression phase allows knowing which part of the image encodes which other

one. To define the score of a domain, the main idea is to know how many ranges may

encode the domain, according to some reconstruction error threshold

S . The recon-

struction error of a range

by a domain D according to a given transform

defined by

()

(

)

(

)

(

)

[

]

RlciIsosDresisodRDEr

lciIsos

,.,,min,

,,,

(3)

Where

()

.,.d is the RMS measure between two blocks.

Then, we have

(

)

()

∑

≤

SRDEr

SDscore

(4)

where

⎩

⎨

⎧

otherwise

trueisPif

So we define a score to rate domains. The score of

D is the number of ranges

that verify

()

SRDEr ≤, . We may notice that when contrast is low, the matching

between blocks cannot be considered as representative. As natural images are coher-

ent, the domain score map associated with the image varies practically in a continu-

ous way. Then we can say the pertinence of a domain is not located exclusively on

this domain location but expands to its neighborhood. This phenomenon ensures the

robustness of our method, regardless of small variations according to the choice of a

partition of the initial image.

We have just seen how to rate domains. This allows us to establish an order relation

among the domains and gives us a choice criterion, so that the learning process leads

to a good modeling of the image or texture.

3.2 Selection of Representative Domains

We want to characterize textures through most representative domains. A quite natu-

ral idea would be to select domains with high scores. From the previous remark, we

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notice two neighboring domains may have similar scores and would be too redundant

to be both kept. They may encode parts of the same zone of the image. In fact, we

want to encode the biggest possible area of the image with the smallest number of

domains. If we want a good quality of reconstruction, we must store a huge number

of domains. Otherwise, if we do not want our learning to be too specific, we must

select few of them. So, the selection of representative domains was made in two

steps:

• Computation of the threshold

S according to the needed percentage of cov-

erage.

• Iterative selection of the best domains, according to reconstruction thresh-

old

Thanks to the matrix

(.,.)Er

we know the minimum threshold needed for a particu-

lar range be encoded by at least one domain. We compute

so that each element of

a set of ranges, which represents the needed coverage percentage, can be encoded by

at least one domain. Then we proceed as follow for the iterative selection of the do-

mains:

1. All ranges are marked not encoded

2. We compute the score of each domain according to

S , taking into account

only non-encoded ranges

3. The domain with the best score is selected and stored. All the ranges it en-

coded are marked encoded.

Repeat 2 and 3 until the needed percentage is reached.

3.3 Inter Class Discrimination

In order to achieve the segmentation of an image, several textures have to be learnt

using the previous method. When learning a texture, some domains can be ambiguous

as they allow encoding parts of other textures. These domains may be prejudicial to

the discrimination. To suppress this ambiguity, we try to reconstruct a texture (i.e. its

ranges) with domains coming from other textures. A domain from a texture is consid-

ered as ambiguous if it allows encoding more than a given percentage of ranges of

another texture. The ambiguous domains are suppressed from the learning data base.

4 Recognition and Experimentation

4.1 Global Method

For each learnt texture, we have a set of representative domains. We want to use these

blocks to label each pixel of a new image. To do so, we compare each square part of

the image with each domain of each concept. This comparison relies on a distance

between one domain of a concept and the block of the considered image (this distance

will be defined below). Thus, for a new image, we compute a distance map (same

dimension as the image) to each texture or concept. To increase the reliability of our

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results, we included some neighborhood information: one can be more confident in an

area reconstructed with many different domains of a concept rather than another area

reconstructed with a very few of them. This parameter is called richness of domains.

Let

c be a learned concept and

a pixel on the test image,

(

)

cprich , is the

number of different domains of

c used to reconstruct the neighborhood of

. The

more the richness is high and the distance is small, the more we are confident in the

labeling of the considered area.

4.2 Distance Definition for the Recognition Task

The principal originality of this study is the learning process. Once the reference

block database has been computed for each concept, the local recognition task can be

summarized as a classical search of the nearest element between the blocks of the test

image and each domain of each concept. The local distance is computed by taking the

normalized best output of a neural network, which take simple features as input (gra-

dient orientation, histogram richness, fractal dimension, standard deviation). When

using both richness and local distance parameter, we must normalize them in order to

compute a coherent value. Let

be a particular pixel. For each class

c we know

the normalized distance

()

cpdist , and the normalized richness

(

)

cprich , . The

final distance of

is defined as

() () ()

(

)

,1,,

iiifinal

cprichcpdistcpd −+=

(5)

And so the label of

will be:

()

(

)

(

)

ifinali

cpdplabel ,minarg

(6)

4.3 Learning Data and Test Results

We have chosen to learn two classes: building and vegetation. To illustrate our

method we will learn 4 classes. Each learning sample contains between 4000 and

18000 domains. We will store the up to 30 most representative domains for each

class.

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Fig. 1. Learning data base sample. Areas 1 & 2 contain vegetation; areas 3 & 4 contain build-

ings.

Fig. 2. A test image and its ground truth of the test image: white for buildings, black for vege-

tation.

Fig. 3. Buildings recognition without (upper) and with (lower) richness parameter on the left

and right respectively.

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Table 1. Confusion matrix without richness parameter.

Vegetation Buildings

Vegetation 98.6 1.4

Buildings 45.4 54.6

Table 2. Confusion matrix with richness parameter.

Vegetation Buildings

Vegetation 98.6 1.4

Buildings 41.8 58.2

These confusion matrixes are computed pixel per pixel, which is not very favorable to

estimate small object segmentation. So we may notice that:

• 100% of the connected building regions have been detected.

• There is only 1 false alarm (on the bottom right of the image) for building

recognition with the richness parameter.

• Small isolated buildings are still detected when using the richness parameter.

5 Conclusion & Perspectives

We have presented an original idea for texture concept learning using the principle of

fractal compression. This type of learning takes only a few parameters as inputs (per-

centage of texture coverage, number of maximum stored domains) that do not need

any specialist’s skills to be set. Then we have seen a simple recognition step and

some test results. We highlighted the gain of reliability when taking into account the

richness of the classes’ elements used to reconstruct an area. As this is some of our

first test results, the test database size must still be increased to qualify our approach.

Concerning the recognition step, others solutions are available, using for example the

normalized inter correlation to precisely locate similar domains in a straight forward

way.

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