A multi color space approach for texture classification: experi-ments with Outex, Vistex and Barktex image databasesA. Porebski (1, 2), N. Vandenbroucke (1, 2) and L. Macaire (2)

alice.porebski@eipc.fr, nicolas.vandenbroucke@eipc.f r, ludovic.macaire@univ-lille1.fr

(1) Ecole d’Ing enieurs du Pas-de-CalaisDepartement AutomatiqueCampus de la Malassise62967 Longuenesse Cedex - FRANCE

(2) Laboratoire LAGIS - UMR CNRS 8146Universit e Lille 1 - Sciences et TechnologiesCit e Scientifique - B atiment P259655 Villeneuve d’Ascq - FRANCE

AbstractThe color of pixels can be represented in different color

spaces which respect different properties. Many authors havecompared the classification performances reached by these colorspaces in order to determine the one which would be the wellsuited to color texture analysis. However, the synthesis oftheseworks shows that the choice of the color space depends on theconsidered texture images. Moreover, the prior determination ofa color space which is well suited to the considered class discrim-ination is not easy. That is why we propose to consider a multicolor space approach designed for color texture classification. Itconsists in selecting, among a set of color texture featuresex-tracted from images coded in different color spaces, those whichare the most discriminating for the considered color textures. Inthis paper, we experimentally study the contribution of this multicolor space with three well-known benchmark databases, namelyOutex, Vistex and Barktex. Comparison and discussion are thencarried out.

IntroductionMany authors have shown that the use of color improves

the results of texture classification compared with grey level im-age analysis [4, 8, 11, 3]. However, there exists a large numberof color spaces which respect different properties and it iswellknown that the choice of the color space impacts the quality ofcolor texture classification. That is why many authors have com-pared the performances reached by classification schemes operat-ing in different color spaces in order to determine the best suitedone to color texture analysis [4, 8, 11, 18, 6, 20]. The synthesisof these works shows contradictions that do not allow to concludeon the definition of a single color space well suited to the dis-crimination of all the textures. Moreover, the prior determinationof a color space which is well suited to the considered texturediscrimination, is not easy. That is why we propose to experi-mentally study in this paper a multi color space approach wherethe properties of several different color spaces are simultaneousconsidered in order to analyze any color textures. For this pur-pose, color texture features are first computed from images codedin several color spaces. The most discriminating color texturefeatures are then selected thanks to a sequential feature selection

procedure achieved during a supervised learning scheme. Inthispaper, we propose to measure the contribution of the multi colorspace approach by comparing the classification results reached bya single color space approach with those obtained by the proposedmulti color space approach. In order to assess these results, thisexperimental study is carried out on three well-know benchmarkdatabases: Outex, Vistex and Barktex [10, 12, 7].

In the second section of this paper, we present the influenceof the choice of color spaces on texture classification. Then, inthe third section, we describe the color texture features used inthis paper to carry out our experiments. The fourth section detailsthe used texture classification method, which is based on these-quential selection of a low dimensional feature space. In the fifthsection, the set of images coming from the OuTex, VisTex andBarkTex image databases and used in our experiments are pre-sented. Finally, experimental results show the contribution of themulti color space approach for color texture classification.

Color spacesThe color of pixels can be represented in different color

spaces which respect different physical, physiologic, andpsycho-visual properties [2]. Figure 1 shows several spaces com-monly used in color image analysis. They can be classified intofour families: the primary spaces, the luminance-chrominancespaces, the perceptual spaces and the independent color compo-nent spaces [2].

Many authors have tried to compare the classification perfor-mances obtained with different color spaces [4, 8, 11, 18, 6,20].Their goal is to determine the color space which would be thebest suited to their color texture classification application. How-ever, the synthesis of these works shows that there is no colorspace which is adapted to the discrimination of all the textures.For example, the conclusions of Maenpaa draw that the choice ofcolor space choice depends on the considered texture images. In-deed, by applying the same color texture classification method tothe images of the VisTex and OuTex databases, he shows that the(L∗

,a∗,b∗) perceptually uniform color space allows to obtain bet-ter results than the(R,G,B) acquisition color space when the Vis-Tex texture images are classified, and inversely when the OuTexones are considered [8]. By applying other classification methods

(R,G,B)

(X,Y,Z)

(x,y,z)

(r,g,b)

(A,C1,C2)

(bw,rg,by)

(Y′, I ′ ,Q′)

(Y′,U ′

,V ′)

(L∗,a∗,b∗)

(L∗,u∗,v∗)

(L,U,V)

(Y,x,y)

(I1,r,g)

(I1, I2, I3)

(A,CC1C2,hC1C2)

(bw,Crgby,hrgby)

(I1,CI2I3,hI2I3)

(Y′,C′

IQ,h′IQ)

(Y′,C′

UV ,h′UV )

(L∗,C∗

ab,hab)

(L∗,C∗

uv,huv)

(L,CUV ,hUV )

(L∗,S∗uv,huv)

(I1,S1,H3)

(I5,S4,H2)

(I4,S3,H2)

(I1,S2,H1)

(I1,S1,H1)

Primary spaces

Luminance-chrominance spaces

Independentcomponent spaces

Perceptual spaces

Real and artificialprimary spaces

Normalizedcoordinate spaces

Antagonistspaces

Televisionspaces

Perceptuallyuniform spaces

Other luminance-chrominance spaces

Polarcoordinate spaces

Perceptualcoordinate spaces

1

2

3

4

5

6

7

8

1

2

3

4

5

6

7

8

Figure 1. Color space families.

and by using other texture features, the best classificationresultsare obtained with the(I4,S3,H2) perceptual color space for theOutex database and with the(I1, I2, I3) independent componentcolor space for the Vistex Database.

On the one hand, when one considers several available colorspaces, it is difficult to select a specific those which are well suitedto all the color image analysis applications. On the other hand, theperformance of an image classification procedure depends onthechoice of the color space.

To solve this problem, few works propose to combine differ-ent color spaces or color components in order to improve colorimage classification performance. Chindaro uses a color textureclassification system based on a set of independent classifiers.Each of these classifiers is assigned to a specific color space. Inorder to classify images, he fuses the classification decisions ofthe classifiers. He concludes that the association of informationscoming from different color spaces improves performances [3].

Porebski proposes to code color texture images into severaldifferent color spaces and to compute some texture featuresfromthe so-coded images. A feature selection procedure selectsthemost discriminating color texture features for the textureclassifi-cation. She proposes an approach that selects the most discrimi-nating Haralick features extracted from chromatic co-occurrencematrices of color images coded in 28 different color spaces thanksto a sequential forward selection [14].

In this paper, we propose to experimentally compare the per-formance of a multi color space approach where theNS= 28 color

spaces of figure 1 are used with the performance reached by a sin-gle color space approach.

In order to take into account all the properties of thesespaces, we propose to extract color texture features from imagescoded in theseNS= 28 spaces and to select the most discriminat-ing ones in order to classify textures images.

Color texture featuresFor the texture classification purposes, a color texture is de-

scribed by a set ofd features and is represented by a point in ad-dimensional feature space in order to achieve the classification.There exists a large number of color texture descriptors andit iswell known that the performance of the classifier depends on thechoice of the color texture feature but also on the dimensionofthe feature space. Indeed, a too high dimensional feature spacecan decrease the rate of well-classified textures and increase thecomputation time.

In order to represented both the color and texture informa-tions, we need to use color texture features which both take intoaccount the color distribution in the color space and the spatialinteraction between pixels in the image. Moreover, becausethecolor information is projected in a three-dimensional color space,we need features which consider spatial relationships within andbetween different color components in order to completely de-scribe the information represented by the whole color texture.

Even if many color texture features can be used, we haveshown that the well known Haralick features extracted from Re-duced Size Chromatic Co-occurrence Matrices (RSCCM) are rel-evant for this purpose [17]. That is why, we choose these featuresto be easily implemented in order to carry out our experimentalstudy.

RSCCMs areN×N co-occurrence matrices where the quan-tization levelN of the color components is reduced in order todecrease the memory storage cost and so, the time required toex-tract features from these matrices. In our experiments,N is set to16. It has been proved that the choice of the neighborhood shapeused to compute RSCCM depends on the analyzed textures [15].When textures do not present any privileged direction, it isnec-essary to compute features which take into account all the pos-sible directions. That is why we choose to use the 3x3 isotropic8-neighborhood in order to take into account all directions.

Each color imageI , coded in a (C1,C2,C3) color space of thefigure 1, is characterized by theNM = 6 following RSCCMs:

• three within color component matrices:mC1,C1[I ], mC2,C2[I ] andmC3,C3[I ].

• three between color component ones:mC1,C2[I ], mC1,C3[I ] andmC2,C3[I ].

Usually, RSCCMs are not directly exploited because theycontain a large amount of information. To reduce it, while pre-serving the relevance of these descriptors, we use the firstNH =13Haralick features denotedf1 to f13 extracted from theses matri-ces [5].

In order to exploit all the properties of theNS color spaces,each image is firstly coded in each of these color spaces. Then,for each color space, theNM = 6 RSCCMs are computed and theNH = 13 Haralick features are extracted from each matrix. Acolor texture is firstly represented byNf = NH ×NM ×NS= 13×6×28= 2184 candidate color texture features (see. figure 2).

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13 Haralick features13 Haralick features13 Haralick features13 Haralick features13 Haralick features13 Haralick features

mR,R

mG,G

mB,B

mR,G

mR,B

mG,B

(R,G,B)

13 Haralick features13 Haralick features13 Haralick features13 Haralick features13 Haralick features13 Haralick features

mL∗,L∗

mu∗ ,u∗

mv∗ ,v∗

mL∗ ,u∗

mL∗ ,v∗

mu∗ ,v∗

(L∗,u∗,v∗)

Other color spaces. . .

IMAGE

Figure 2. Candidate color texture features

Because the dimension of the feature space is too high, itis necessary to select the most discriminating color texture fea-tures during a supervised learning stage. For this purpose,weuse a sequential forward selection (SFS) procedure associated toa measure of the discriminating power of a candidate color tex-ture feature space [14]. The next section details the supervisedclassification scheme used in our experimental study.

Texture classification schemeIn order to carry out our experimental study, we propose to

apply a supervised classification scheme divided into two succes-sive stages:

• A supervised learning stage selects a low number of dis-criminating texture features among a set of candidate onesin order to build a feature space thanks to a feature selec-tion procedure. During this stage, the classifier is trainedtopartition this feature space.

• A decision stage where the examined color texture is repre-sented by a point in the so-selected feature space.

For this purpose, each set of images used in experiments isdivided into two sets: the training set and the test set. For eachset, the training and the test sets are built according to theHoldoutmethod: half of the images are used to build the training set,whilethe other images are used to test the performance reached by theclassifier.

Feature selectionThe determination of the most discriminating feature spaceis

achieved thanks to a sequential feature selection procedure basedon a supervised learning scheme. This non-exhaustive search pro-cedure has given very encouraging results for color image classi-fication of the Outex, Vistex and Barktex databases [17, 14].

At each steps of this procedure, an informational criterionJs is evaluated in order to measure the discriminating power ofeach candidate feature space. At the beginning of this procedure(s= 1), theNf one-dimensional candidate feature spaces, definedby each of theNf available color texture features, are examined.The candidate feature which maximizes (or minimizes)J1 is se-lected at the first step. It is associated in the second step ofthe pro-cedure (s= 2) with each of the(Nf −1) remaining candidate colortexture features in order to constitute(Nf − 1) two-dimensionalcandidate feature spaces. We consider that the two-dimensional

space which maximizes (or minimizes)J2, is the best space fordiscriminating the texture classes. . .

In order to only select color texture features which are notcorrelated, we measure, at each steps≥ 2 of the procedure, thecorrelation level between each of the available color texture fea-tures and each of the(s−1) other color texture features constitut-ing the previously selected(s−1)-dimensional space. The con-sidered features will be selected as candidate ones only if theircorrelation level with the already selected color texture features islower than a threshold fixed by the user [19].

We assume that the more the clusters associated with the dif-ferent texture classes are well separated and compact in thecan-didate feature space, the higher the discriminating power of theselected color texture features is. That leads us to choose mea-sures of class separability and class compactness as measures ofthe discriminating power.

The measure of compactness of each texture classTj is de-fined by the within-class dispersion matrixΣC. The measure ofthe class separability is defined by the between-class dispersionmatrix ΣS. The most discriminating feature space minimizes theWilks’s criterion:

Js =|ΣW|

|ΣC+ΣS|.

At each step of the sequential selection procedure, a fea-ture is added and the dimensiond of the selected feature spaceincreases. This procedure is iterateddmax times in order tobuild dmaxd-dimensional pre-selected color texture feature spaces(1≤ d≤ dmax). In order to determine the dimensiond of the mostdiscriminating feature space, we propose to measure the rate Tdof well-classified images obtained with eachd-dimensional pre-selected space. The selected color texture feature space isthepre-selected space for whichTd is maximum:

d =dmax

argmaxTdd=1

Usually, the images of the training set are used to determinetherate of well-classified images. However, when an image is ex-tracted from this set to be classified, the partition of the featurespace can be modified, for example when a k-nearest neighborclassifier is used. That is why, we propose a different approachwhere another set of images is used to measure the rate of well-classified images. Here, the test set is used for this purpose.

ClassificationDuring the decision stage, we propose to classify the im-

ages of the test set thanks to the nearest neighbor (1-NN) classi-fier which is a simple and widely used classifier. This classifieroperates in the selectedd-dimensional feature space.

Experimented texture databasesIn this section, we present three well-known and largely used

benchmark color texture databases used in our experiments andmore precisely, three different benchmark texture sets, comingfrom OuTex, VisTex and BarkTex databases, respectively [10, 12,7].

OuTex databaseOutex contains a very large number of surface textures ac-

quired under controlled conditions by a 3-CCD digital colorcam-

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era. To build the Outex set, each of 68 textures of this databaseis split up from an original image into disjoint sub-images whosesize is 128×128 pixels. Since the original image size is 746×538pixels, this makes a total of 20 sub-images by texture. Figure 3illustrates each class of the used Outex texture by one image.

Figure 3. Example of OuTex color textures: each image represents a class

of texture.

The examined dataset is available at the OuTex web site(http://www.outex.oulu.fi/temp/) as test suite Contrib-TC-00013. Among the 1360 images of the Outex set, 680 imagesare used for the training set and 680 for the test set [13, 8, 1,6, 20].

VisTex databaseVistex is a large set of natural color textures acquired under

non-controlled conditions by different color cameras. To buildthe Vistex set, each of 54 textures of this database is split up froman original image into disjoint sub-images whose size is 128×128 pixels. Since the original image size is 512×512 pixels, thismakes a total of 16 sub-images by texture (see figure 4).

Figure 4. Example of VisTex color textures: each image represents a class

of texture.

This set is available at the OuTex web site

(http://www.outex.oulu.fi/temp/) as test suite Contrib-TC-00006. Among the 864 images of the Vistex set, 432 imagesare used for the training set and 432 for the test set [13, 8, 1,6].

BarkTex databaseColor images of the BarkTex database are equally divided

into six tree bark classes, with 68 images by class. The size ofeach image is 256×384 pixels.

To build the BarkTex set, a region of interest, centered on thebark and whose size is 128×128 pixels, is firstly defined. Then,four sub-images whose size is 64×64 pixels are extracted fromeach region. We thus obtain a set of 68×4= 272 sub-images byclass (see figure 5).

Figure 5. Example of BarkTex color textures: each column represents a

class of texture.

Among the 1632 images of the Barktex set, 816 images areused for the training set and 816 for the test set.

Experimental resultsIn order to examine the contribution of the multi color space

approach on the quality of texture classification, the proposedclassification method is separately applied to the texture setswhich have been previously presented.

For this purpose, we propose to characterize the textures ac-cording to different ways:

• Haralick features are computed from grey level co-occurrence matrices. In these cases, only the luminancecomputed from(R,G,B) color is analyzed. So, only 13 fea-tures are considered.

• Haralick features are extracted from RSCCMs computedfrom images coded in a single color space. In these cases,d features are selected among 13× 6 available ones. Wefirstly measure the classification results obtained by exam-ining images coded in the(R,G,B) acquisition color space.We also evaluate the classification results obtained by con-sidering images coded in the color space for which the bestrate of well-classified images is obtained by other authors.

• Haralick features are extracted from RSCCMs computedfrom images coded in theNS = 28 different color spacesof figure 1. In these cases, the feature selection scheme de-terminesd features among 13×6×28 available ones.

Figures 6, 7 and 8 illustrate the classification results obtainedby our approach applied to OuTex, VisTex and BarkTex color tex-ture image databases, respectively. These figures display,for thefour different ways to characterize color textures, the ratesT of

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well-classified images of the test set, with respect to the dimen-siond of the feature space built by the feature selection procedure.

Let us notice that when the color textures are characterizedby grey level features, the numberNf of candidate features doesnot exceed 13. The best rates of well-classified images for the Ou-tex and Vistex sets are obtained by Maenpaa with the(I4,S3,H2)and(I1, I2, I3) color spaces respectively [8]. To obtain these re-sult, the color histograms are directly used as features. For theBarktex set, Munzenmayer obtains a rate of well-classifiedim-ages of 87 % with the(R,G,B) color space and by using featuresextracted from interplane sum- and difference-histograms[9].

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 500

10

20

30

40

50

60

70

80

90

100

d

T

Muti color space(I4,S3,H2)(R,G,B)Luminance

Figure 6. Rates T(%) of well-classified images obtained with the OuTex

test set according to the dimension d of the selected feature space.

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 500

10

20

30

40

50

60

70

80

90

100

d

T

Muti color space(I1,I2,I3)(R,G,B)Luminance

Figure 7. Rates T(%) of well-classified images obtained with the VisTex

test set according to the dimension d of the selected feature space.

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 500

10

20

30

40

50

60

70

80

90

100

d

T

Muti color space(R,G,B)Luminance

Figure 8. Rates T(%) of well-classified images obtained with the BarkTex

test set according to the dimension d of the selected feature space.

Globally, we can notice that the classification results tendtostabilize whend is higher than 10.

The classification results obtained with features extractedfrom images coded in a single color space outperform those ob-tained grey level features. So, the use of color significantly in-creases the quality of texture classification.

On the other hand, the comparison of the classification re-sults reached with the Outex and Vistex sets by considering the(R,G,B) color space with those selected by Maenpaa, showsthat choosing a relevant color space to discriminate color textureclasses significantly improves the classification results.However,the prior choice of such a space is difficult and the feature se-lection procedure is thus an efficient way to determine the mostdiscriminating feature space.

Finally, the difference between the classification resultsreached by considering the relevant color space and those ob-tained with theNS= 28 color spaces shows the advantage of con-sidering the properties of different color spaces for texture dis-crimination.

We propose to compare in depth results obtained with theOutex database (see table 1).

Comparison between different rates of well-classified imag esreached with the Outex test set.

Texture features Color space d Classifier T (%)

Haralick features from

RSCCM (N = 16)

Multi color

space

38 1-NN 95.4

Haralick features from

RSCCM (N = 16)

(I4,S3,H2) 20 1-NN 91.9

Haralick features from

RSCCM (N = 16)

(R,G,B) 45 1-NN 88.1

Haralick features from

RSCCM (N = 16)

Luminance 13 1-NN 72.9

Color histogram [8] (I4,S3,H2) 4096 1-NN 95.4

Color histogram [13] (R,G,B) 4096 3-NN 94.7

Haralick features from

RSCCM (N = 32) [1]

(R,G,B) 30 5-NN 94.9

LBP histogram [6] (I4,S3,H2) 4608 SVM 93.5

features from wavelet

transform [20]

(R,G,B) 5 7-NN 85.2

This table firstly shows that the multi color space approachallows to take advantage of the properties of each color space andso, to significantly improve the classification results compared tothe use of the properties of a single color space beforehand cho-sen or of the single grey level information. It also constitutes anunified approach to classify the color textures.

The results obtained with the proposed multi color space ap-proach (T is equal to 95.4%) are equal to those obtained by usingcolor histogram [8]. We can notice that the dimensiond of thefeature space extracted by our multi color space approach (d isequal to 38) is lower than that of the single color space (d is equalto 4096). So, the time required by the decision stage is lowerbyusing our multi color space approach. This property is very inter-esting for industrial applications which present strong real-timeconstraints about on-line texture classification.

However, color histogram is not considered as a texture fea-ture because it does not take into account the spatial relationship

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between pixels in the image. That is why, texture features cou-pled with statistical features about color distribution seem to beefficient to classify images of the Outex database.

ConclusionIn this paper, we have shown the interest of considering sev-

eral color spaces for color texture classification and have mea-sured the contribution of this multi color space approach with re-gard to texture classification results.

For this purpose, textures have been characterized by Har-alick features extracted from RSCCMs computed from imagescoded inNS = 28 different color spaces and the most discrimi-nating color texture features have then been selected thanks to asequential feature selection procedure performed during asuper-vised learning.

Experimental results achieved with OuTex, VisTex andBarkTex databases have shown that the multi color space ap-proach allows to increase significantly the results of colortextureclassification compared with a single color space chosen accord-ing to a relevant way.

Taking into account the properties of different color spacesallows to follow an unified approach to classify the textures. How-ever, this approach is more time consuming than the single colorspace approach. In order to generalize our approach, we arepresently working on coupling Haralick features with otherwell-known features like LBP (Local Binary Pattern) features [16].

AcknowledgementsThis research is funded by ”Pole de Competitivite Maud”

and ”Region Nord-Pas de Calais”.

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Author BiographyAlice Porebski, Engineer (Ecole d’Ingenieurs du Pas-de-Calais) is

a PhD student at the University of Lille 1, France. Her research topics

concern color texture classification and quality control for industrial ap-plication.

Nicolas Vandenbroucke received the PhD in computer scienceand

control from the University of Lille 1 in 2000. Since 2002, heis an asso-ciate professor in the Ecole d’Ingenieurs du Pas-de-Calais. His researchinterests include color representation and color image analysis applied to

segmentation and classification.Ludovic Macaire is full professor at the University of Lille1. His

research interests deal with color imaging.

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