A Colour Space Selection Scheme dedicated to

Information Retrieval Tasks

Romain Raveaux, Jean-Christophe Burie and Jean-Marc Ogier

L3I Laboratory – University of La Rochelle, France

Abstract. The choice of a relevant colour space is a crucial step when dealing

with image processing tasks (segmentation, graphic recognition…). From this

fact, we address in a generic way the following question: What is the best

representation space for a computational task on a given image? In this article, a

colour space selection system is proposed. From a RGB image, each pixel is

projected into a vector composed of 25 colour primaries. This vector is then

reduced to a Hybrid Colour Space made up of the three most significant colour

primaries. Hence, the paradigm is based on two principles, feature selection

methods and the assessment of a representation model. The quality of a colour

space is evaluated according to its capability to make colour homogenous and

consequently to increase the data separability. Our framework brings an answer

about the choice of a meaningful representation space dedicated to image

processing applications which rely on colour information. Standard colour

spaces are not well designed to process specific images (ie. Medical images,

image of documents) so a real need has come up for a dedicated colour model.

1 Introduction

Colour representation is the basement of all colour image processing applications. In

fact, many colour spaces were developed for graphics and digital image processing

such as Red, Green, Blue (RGB) and Hue, Saturation. Intensity (HSI). Nevertheless, it

is obvious that the performance of any colour-dependent system is highly influenced

by the colour model it uses. The quality of a colour model is defined by its capacity to

correctly distinct colour between them while being robust to variations inside a given

chromatic cluster such as light changes. In term of datamining, this problem can be

addressed as maximizing the distance inter-classes while minimizing the distance

intra-class. These two criteria seem to be conflicting, which represents a real

challenge to any colour representation scheme. Many information retrieval

applications would benefit for a better representation space. The paper is organized as

follows: In the second section, the question of finding the best colour space is

introduced with a review of the related work. Thirdly, the global concept is described

explaining the methodology of our contribution. Then, the fourth section presents the

feature selection methods in use in this paper. The fifth section presents experimental

results on colour classification according different colour models, in addition a

Raveaux R., Burie J. and Ogier J. (2008).

A Colour Space Selection Scheme dedicated to Information Retrieval Tasks.

In Proceedings of the 8th International Workshop on Pattern Recognition in Information Systems, pages 123-134

 SciTePress

comparative study on cadastral map segmentation is presented. Finally, a conclusion

is given and future works are brought in section 5.

2 Related Work

In this section, reference to previous works on this field of science is done starting by

classical colour spaces to finally present the selection of colour components.

2.1 Standard Colour Spaces

Most of acquisition devices, such as digital cameras or scanners, process signals in the

RGB format. This is why RGB space is widely used in the applications of image

processing. The R primary in RGB corresponds to the amount of the physical

reflected light in the red band. However, RGB representation has several drawbacks

that decrease the performance of the systems which depend on it. RGB space is not

uniform; the relative distances between colours do not reflect the perceptual

differences. Therefore, HSI space has been developed as a closer representation to the

human perception system, which can easily interpret the primaries of this space. In

HSI space, the dominant wavelength of colour is represented by the hue component.

The purity of colour is represented by the saturation component. Finally, the darkness

or the lightness of colour is determined by the intensity component. Eq.(1) shows the

transformation between RGB and HSI spaces [1].

Although the HSI space is suitable for lots of applications based on colour images

analysis, this colour space presents some problems. For example, there are non-

avoidable singularities in the transformation from RGB to HSI, as shown in Eq.(1).

The XYZ colour space developed by the International Commission on Illumination

(CIE) in 1931 [2] is based on direct measurements of the human eye, and serves as the

basis from which many other colour spaces are defined. The YUV colour is used in

the PAL system of colour encoding in analogical video, which is part of television

standards. The YUV model defines a colour space in terms of one luminance and two

chrominance components. Another alternative of YUV is the YIQ which is used in

the NTSC TV standard. On the other hand, Ohta, Kanade, and Sakai [3] have selected

a set of "effective" colour features after analyzing 100 different colour features which

have been used in segmenting eight kinds of colour images. Those selected colour

features are usually names as I1I2I3 colour model. XYZ, YUV and I1I2I3 are non-

uniform colour spaces; therefore CIE has recommended CIE-Lab and CIE-Luv as

uniform colour spaces, as they are non-linear transformation of RGB space [4].

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2.2 Hybrid Colour Spaces

Recently, the question of finding the best colour representation has generated a rich

literature. In [5], a standard colour space is picked-up specifically for a given image

however the process involved does not consider the possibility to combine colour

components from several spaces. To solve this problem, in [6], dominant features

from different colour spaces are selected to construct a DHCS (Decorrelated Hybrid

Colour Space). A Principal Component Analysis (PCA) is performed from the

covariance matrix composed with the total number of the candidate primaries. The 3

most significant axis are selected to reduce rate of correlation between colour

components. On the other hand, our approach aims to maximise one criterion which is

the colour recognition rate (Eq 2) while others methods [7] try to compromise indices

(compacity and classes dispersion) in order to assess the suitability of a colour model.

2) (Eq

PixelsColour #

PixelsColour ClassifiedCorrectly #

Rec =

These indices represent two competitive constraints, in other word, two conflicting

objectives, the improvement of one of them leads to the deterioration of the other.

Each image is like no other, so it deserves a dedicated colour representation. We

believe, it is hardly possible to generalize the colour pixel distribution for a given

image set. So it seems unlikely feasible to apply the same colour space on all the

images contained in a database. Each image must be considered independently. in [8],

soccer players are classified, according to their colour information, using supervised

learning techniques, this training stage supposed to dispose of the user ground truth

which is not often the case, and limit the flexibility of the system. Our framework is

generic since it relies on a parsimonious use of machine learning algorithms.

Furthermore, we handle different feature selection methods, we take advantages of

their different ways to reach a single goal.

3 Methodology

The main architecture of our framework is presented in figure 1. It starts from an

RGB image where each pixel is projected into nine standard colour spaces in order to

build a vector composed of 25 colour components. Let C be a set of colour

components.

{} { }

*,...*,*,,3,2,1,,,

vuLIIIBGRCC

with Card(C)=25. From

this point, pixels represent a raw database, an Expectation Maximization (EM)

clutering algorithm is performed on those raw data in order to label them. Each

feature vector is tagged with a label representing the colour cluster it belongs to.

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Fig. 1. A framework for colour space selection.

4 Feature Selection Methods

The selection of features is a very active area in recent years, especially in the context

of data mining. Indeed, the data mining in very large databases is becoming a critical

issue for applications such as image processing, finance, etc. It is important to

summarize and intelligently retrieve the "knowledge" from raw data. The data mining

is an area based on statistics, machine learning and the theory of databases. The

variable selection plays an important role in data mining especially in the preparation

of data prior to processing. Indeed, the interests of the variable selection are as

follows:

- When the number of variables is just too great learning algorithm can not finish in a

good time. The selection reduces the dimension of feature space.

- In terms of artificial intelligence, creating a classifier returns to create a model for

the data. However, a legitimate expectation for a model is to be as simple as possible

(principle of Occam's razor [9]).

Sub Sampling

RGB Image Illuminant

Clustering : EM

Projection in Standard

Spaces

Features selection

Evaluation

1-NN Classification

Pixels In RGB Space

Pixels projected

in 9 spaces

1 pixel = 1 vector with 25

colour components

A set of data

Training set

Test set

Sets of selected features.

Hybrid Spaces

Confusion

rate for each

space

Labeled data

bases

-9 Standard spaces :

(L*a*b*,I1I2I3,..)

K significant clusters are found and split into 2 labeled DB

Labeled data

bases

- CFS

- Genetic Search

- PCA Space

- One R

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Reducing the size of the space feature allows us to reduce the number of required

parameters for the description of this model also avoiding the phenomenon of over-

fitting and emphasizing the synthesize information.

- It improves the performance of the classification, its speed and power of

generalization.

- It increases the data understanding: a better view of what are the processes that give

rise to them. This selection consists of:

Æ The elimination of independent variables of the class,

Æ The elimination of redundant variables.

4.1 Global Concept

A general structure for selecting features can be offered in the way of figure 2 ([10]).

Up to a certain criterion to be satisfied, sub sets are generated in browsing the feature

space.

Fig. 2. Feature selection architecture.

The subsets generation is a searching process in the subset space of cardinality 2

with N the number of features. All classical searching algorithms can be applied to

that problem. For instance [11] proposes the methods forward addition and backward

elimination (deletion), [12] and [13] have made a good use of evolutionary

algorithms.

4.2 Searching Algorithm and Evaluation

Existing feature selection methods for machine learning typically fall into two broad

categories—those which evaluate the worth of features using the learning algorithm

that is to ultimately be applied to the data, and those which evaluate the worth of

features by using heuristics based on general characteristics of the data. The former

are referred to as wrappers and the latter filters.

1. Wrappers use classification algorithm to evaluate the pertinence of a given sub set

of variables. Genetic Algorithms (GA) dedicated to colour space selection are

wrappers based on heuristics. GA for Colour Space(GACS) encodes its individuals as

vectors limited to three components specifically adapted to the colour representation

[14]. 2. Filters are completely independent from the classification stage. They are

based on statistical concepts: entropy, coherence… A good feature subset is one that

contains feature highly correlated with predictive of the class and yet uncorrelated

with the others [10].

Sub set generation

Evaluation

Validation

Stopping criterion

Starting

Set

yes

127

The Wrappers. Although conceptually more simple than filters, wrappers were

introduced more recently by John, and Kohavi Pfleger in 1994. Their principle is to

generate subsets candidates and to evaluate them thanks to a classification algorithm.

The score or merit will be a combination of a trade-off between the number of

variables eliminated, and the classification rate on a test file. Thus, the “assessment”

stage of the selection cycle is made by a call to the classification algorithm. In fact,

the classification algorithm is called several times for each evaluation because a

cross-validation is frequently used. By its very intuitive principle, this method

generates subsets well suited to the classification algorithm. Recognition rates are

high since the selection takes into account the intrinsic bias of data. Another

advantage is its conceptual simplicity: there is no need to understand how the

induction is affected by the selection of variables, it is sufficient to generate and test.

However, there are three reasons that the wrappers are not a perfect solution. First,

they do not really have theoretical justification for the selection and they do not allow

us to understand the conditional dependencies that may exist between the variables.

On the other hand, the selection process is specific to a particular classification

algorithm and find subsets are not necessarily valid if you change the method of

induction. Finally, and this is the main defect of the method, the calculations quickly

become quite long when the number of variable grows up.

The Filters. Filters don’t have the defects of wrappers. They are much faster, they are

based on more theoretical considerations, it allows a better understanding to the

dependency relationships between variables. But, as they do not take into account the

biases of the classification algorithm, the subsets of variables generated give a lower

recognition rate. To give a score to a subset, the first solution is to give a score to each

variable independently of the others and to do the sum of those scores [OneR

Selection]. The alternative is to evaluate a subset as a whole [12]. We are closer here

learning the Bayesian network structure. There is an intermediary between ranking

and feature subset ranking based on an idea of Ghiselli and used with good results in

the context of the CFS (correlation based feature selection) by Mr. Hall [10]. The

score of a subset is constructed based on correlations variable-class and correlations

variable-variable ( Eq 3):

Equation. 3. Correlation score associated to each feature in CFS method

rkkk

)1( −+

Where

r is the correlation between the summed components and the outside variable

(a given colour cluster – a class), k is the number of components,

r is the average of

the correlations between the components and the outside variable, and

r is the

average inter-correlation between components

This equation express that the merit of a given subset increase if the variables are

highly correlated with the class and it decrease if features are highly correlated

between each others. The idea is to state that a “good” subset is composed of

variables highly correlated with the class (to discard independent variables) and

loosely correlated between them/features (to avoid redundant components). It is an

128

approximation since it only takes into account the interactions of order 1. The

correlation or dependency between two variables can be defined in several ways.

Using the statistical correlation coefficient is too restrictive because it only captures

the linear dependence. However, one can use a test of independence as the statistical

test of

. It is also possible to combine wrapper and filter as presented in [13]

4.3 Stopping Criterion

The stopping criterion may take various forms: a computation time, a number of

generations (for a genetic algorithm), a number of selected variables or a heuristic

evaluation of the subset “value”.

Table 1. Selection feature methods in use.

Name Type Evaluation Searching algorithm

CFS

Filter CFS Greedy stepwise

EHD

Filter PCA

Ranker

GACS

Wrapper Classification Genetic Algorithm

OneRS

Wrapper Classification Ranker

4.4 Hybrid Colour Space built by Genetic Algorithm. GACS

In Hybrid Colour Space (HCS) context, each individual has to encode a vector, where

each component is an axis of the HCS. We consider a set C of features.

{}

{

}

*,...*,*,,3,2,1,,,

vuLIIIBGRCC

with Card(C)=25. Practically, it is

almost impossible to test all possible combinations, since they have a combinatory

number equal to the factorial of the total number of the candidate primaries, hence,

Genetic Algorithm are well suited to get rid off absurd combinations. From now, the

first step is to initialize the population, each individual is made up picking randomly

three elements of C. Concerning cross over operator, two individuals h1 and h2 share

their genetic material, swapping one of their component; fig 3. Finally, to perform

mutation on an individual, one component is selected and replaced at random by an

element of C. Finally, the evaluation phase computes a 1NN classifier based on a

Euclidian metric. A cross-validation system is run on the training base.

Fig. 3. HCS: cross over operator.

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5 Experiments

5.1 Context

In the idea to assess our system, we perform two evaluation stages. The first one is a

colour classification step to test if the colour representation found by our framework

is interesting in term of colour distinction. The second step is a segmentation phase.

Indeed, a better representation system should give better segmentation results.

5.2 Colour Classification

Our approach is applied on three different types of images. A natural scene, an image

of the document and a synthetic image. Each colour space is evaluated on the test

dataset through the use of a 1NN classification step. In table 2, the number of colour

clusters found by the clustering algorithm(EM) is given. Table 4 shows the

components selected by the several feature selection techniques.

Fig. 4. Images in use.

Table 2. Test images description.

Id Image Type # of

clusters

Im1

Lenna Natural Scene 18

Im2

SatSnake Synthetic image,

discriminating

by the saturation

Im3

Image of

document

Ancient

Cadastral Map

Table 3. Training and Test Databases.

training

X pixels

test

X pixels

IM1 130107 130107

IM2 100951 100951

IM3 110424 110424

Table 4. Hybrid Colour Spaces found on the

Image IM2.

Attributes CFS GACS

DHCS

OneRs

R 0 0 0 0

G 1 0 1 0

B 1 0 0 0

I1 0 0 0 0

I2 1 0 0 0

I3 0 0 0 0

T 1 1 0 1

S 1 1 0 1

I 0 0 0 0

L* 1 0 0 0

a* 1 0 0 0

b* 1 0 0 0

L* 0 0 0 0

u* 1 1 0 0

v* 1 0 0 0

A 0 0 0 0

C1 1 0 0 0

C2 1 0 0 0

X 0 0 0 0

Y 0 0 1 0

Z 1 0 1 0

Y 0 0 0 0

I 1 0 0 1

Q 1 0 0 0

Y 0 0 0 0

U 1 0 0 0

V 0 0 0 0

# of

attributes

16 3 3 3

Table 5. Number of selected features.

# of selected attributes

CFS GACS EHD OneRS

IM1 16 3 3 3

IM2 16 3 3 3

IM3 12 3 3 3

130

Table 6. Confusion rate on Image 1.

IM1

Colour

Spaces

Error ColourSpaces Error

RGB

0.3608

TSI

0.3917

I1I2I3

0.3814

La*b*

0.4329

XYZ

0.3814

L*u*v*

0.4948

YIQ

0.4742

DHCS

0.3917

YUV

0.3195

CFS

0.0615

AC1C2

0.4123

GACS 0.2680

PCA

0.3711

OnRS

0.3608

Table 7. Confusion rate on Image 2.

IM2

Colour Spaces Error Colour Spaces Error

RGB

0.22

TSI

0.51

I1I2I3

0.24

La*b*

0.41

XYZ

0.23

L*u*v*

0.35

YIQ

0.43

DHCS

0.39

YUV

0.35

CFS

0.14

AC1C2

0.29

GACS 0.19

OnRS

0.39

PCA

0.35

Table 8. Confusion rate on Image 3.

IM3

Colour Spaces Error Colour Spaces Error

RGB

0.5444

TSI

0.3666

I1I2I3

0.2222

La*b*

0.2666

XYZ

0.5777

L*u*v*

0.3333

YIQ

0.3111

DHCS

0.36

YUV

0.3777

CFS

0.0333

AC1C2

0.3

GACS 0.1888

OnRS

0.4111

PCA

0.2444

5.3 Application to Segmentation and Evaluation

Once the source image is transferred into a suitable hybrid colour space, an edge

detection algorithm is processed. This contour image is generated thanks to a vectorial

gradient according to the following formalism. The gradient or multi-component

gradient takes into account the vectorial nature of a given image considering its

representation space (RGB for example or in our case hybrid colour space). The

vectorial gradient is calculated from all components seeking direction for which

131

variations are the highest. This is done through maximization of a distance criterion

according to the L2 metric, characterizing the vectorial difference in a given colour

space. The approaches proposed by DiZenzo[7] first, and then by Lee and Cok under

a different formalism are methods that determine multi-components contours by

calculating a colour gradient from the marginal gradients.

Given 2 neighbour pixels P and Q characterizing by their colour attribute A, the

colour variation is given by the following equation:

)()(),( PAQAQPA

−

The pixels P and Q are neighbours, the variation

can be calculated for the

infinitesimal gap: dp = (dx, dy)

∂

This differential is a distance between pixels P and Q. The square of the distance is

given by the expression below:

()()()

332211

GGGc

GGGGGGb

GGGa

cdybdxdyadx

dxdy

++=

⎟

⎠

⎞

⎜

⎝

⎛

∂

⎟

⎠

⎞

⎜

⎝

⎛

∂

Where, E can be seen as a set of colour components representing the three primaries

of the hybrid colour model. And where

G can be expressed as the marginal

gradient in the direction

n for the m

colour components of the set E.

The calculation of gradient vector requires the computation at each site (x, y): the

slope direction of A and the norm of the vectorial gradient. This is done by searching

the extrema of the quadratic form above that coincide with the eigen values of the

matrix M.

⎟

⎠

⎞

⎜

⎝

⎛

The eigen values of M are:

⎟

⎠

⎞

⎜

⎝

⎛

+−±+=

4)(5.0 bcaba

Finally the contour force for each pixel (x,y) is given by the following relation:

−+

−=

λλ

),( yxEdge

These edge values are filtered using a two class classifier based on an entropy

principle in order to get rid off low gradient values. At the end of this clustering stage

a binary image is generated. This image will be called as contour image through the

rest of this paper. Finally, regions are extracted by finding the white areas outlined by

black edges. In order to compare the results between the segmented image generated

by the computer and the user defined ground truth, the Vinet criterion[15] is chosen

[Tab 9].

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Table 9: Segmentation evaluation on Hybrid Colour Space.

Dizenzo Segmentation

Colour Cadastral maps

# of regions Vinet

criterion

RGB image 1714 0.5703

HCS found by GACS 1596 0.5821

Another way to assess a segmentation process is to compute the Levin and Nazif (LN)

criterion. It takes into parameters the segmented image and the original image and

returns a score, the higher the better. This comparison is carried out on a set of 50

maps. Levin and Nazif criterion[15] is the union of two principles, the disparity intra

and inter regions.

Table 10 : Comparison HCS and RGB spaces on a segmentation process using LN criterion.

Dizenzo Segmentation

Colour Cadastral maps

LN Criterion on 50 images

Average Std deviation

RGB 0.4770375 0.005396543

HCS 0.480325 0.007211647

5.4 Analyze

The quality of a colour model is judged by two decisive factors: "Robustness" and

"Distinction". The robustness of the colour representation is an indication of the

sensitivity of colour values to illumination and brightness variations. The

“Distinction” capacity of a colour model is directly linked to its capacity to separate

one colour to the others.

The colour space minimizing the error rate classification is the most discriminating

space for a given image [Tab 6,7,8]. The space generating the least mistake will be

retained to continue treatments on the image. The chosen space is minimizing the

distance intra-class, within the same unit chromatic while maximizing the distance

inter-classes. Such properties are helpful in post-processing stages such as

segmentation, or graphics recognition. Thanks to a well suited colour model, the

number of regions has been reduced by 118 decreasing the over-segmentation

problem, moreover, the Vinet criterion has been improved by 2% getting closer to

user ground truth. At the same time, the LN criterion results lead to the same

conclusion, showing that the contrast inter regions and the homogeneity intra-region

are slightly better in HCS than in the RGB case. These results are encouraging and

they demonstrate how important it is to choose a “good” colour model.

6 Conclusions

In this paper, we have presented a colour space selection framework. Our contribution

focuses on a “all-in-one” system to find a suitable colour space. Our tool can be seen

as a pre-process to any colour information retrieval application (Segmentation,

graphic recognition …). Our approach aims to maximise one criterion which is the

colour recognition rate to unleash the colour information. Each image is like no other,

133

so a dedicated colour representation is required. We believe, it is hardly possible to

model a unique colour space from a given image set and then to apply this “mean

model” individually, that’s why our method computes independently a dedicated

model to each image. Our framework relies on a wise use of different feature

selection methods in order to take advantages of their diverse ways to reach a single

goal. Finally, Hybrid Colour Spaces are particularly well suited while dealing with

very specific images, such as medical images, images of documents where CIE spaces

are not particularly well designed. We believe that much colour image software would

get profit to the use of an adapted colour space.

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