All the Truth About NEvAr

18 Pages·2002·0.94 MB·English

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All the Truth About NEvAr
PENOUSALMACHADOANDAM´ILCARCARDOSO
CentrodeInforma´ticaeSistemasdaUniversidadedeCoimbra(CISUC),PinhaldeMarrocos,
3030Coimbra,Portugal
[email protected]
[email protected]
Abstract. TheuseofEvolutionaryComputationapproachestogenerateimageshasreachedagreatpopularity.
This led to the emergence of a new art form—Evolutionary Art—and to the proliferation of Evolutionary Art
Tools.Inthispaper,wepresentanEvolutionaryArtTool,NEvAr,theexperimentalresultsachieved,andthework
methodologyusedtogenerateimages.InNEvAr,usefulindividualsarestoredinadatabaseinordertoallowtheir
reuse. This database is playing an increasingly important role in the creation of new images, which led us to the
developmentofautomaticseedingprocedures,alsodescribed.Theautomationoffitnessassignmentisoneofour
presentresearchinterests.Wewill,therefore,describesomepreliminaryresultsachievedwithourcurrentapproach
toautomaticevaluation.
Keywords: evolutionaryart,geneticprogramming,computationalcreativity
1. Introduction development of an incredible amount and variety of
solutions,species,toaspecificproblem,survival.Itis,
Creativityisoftenregardedasoneofthemostimpres- therefore,unquestionablethatthisprocesscangiverise
sive features of the human mind, which may explain toinnovativesolutions[5].
the current interest of the Artificial Intelligence com- InthepastfewyearstwoEvolutionaryComputation
munityinthestudyofcomputationalcreativity.Several (EC)techniques,namelyGeneticAlgorithms[6](GA)
otherfactors,however,contributetothisrisinginterest: andGeneticProgramming[7](GP),havebeenusedas
artificialcreativesystemsmayproveusefulinawide meanstoimplementcomputationalcreativity,resulting
rangeofartistic,architecturalandengineeringdomains inthedevelopmentofseveralapplicationsinfieldssuch
where conventional problem solving techniques have asmusicandimagegeneration,architectureanddesign.
failed; its study may bring insight to the overall un- GAarethemostcommonECapproachinthemusi-
derstandingofhumancreativity;thestudyofartificial calfield(e.g.[8–11]).However,accordingto[12],and
creativitycanbeviewedasthelogicalnextstepinAI inspiteofthenumerousapplications,GAarenotideal
research,i.e.ifwecanalreadybuildsystemscapableof forthesimulationofhumanmusicalthought.
performing tasks requiring intelligence, can we build In the image generation field, GP is the most used
systemsabletoperformtasksrequiringcreativity? EC approach, some examples being [13–16], which
The development of artificial creative systems is evolve images, and [17], where GP is used to evolve
often inspired in models of the human creative pro- humanfaces.GPhasalsobeensuccessfullyappliedin
cess (e.g. [1–4]). There are, however, other possible thefieldsofdesign[18,19]andanimation[14,16,20].
sourcesofinspiration.Evolutionisresponsibleforthe The main difficulty in the application of EC ap-
proachestofieldssuchasimageandmusicgenerationis
∗This work was partially funded by the Portuguese Ministry of thedevelopmentofanappropriatefitnessfunction.Asa
ScienceandTechnology,underProgramPRAXISXXI. result,mostsystemsrelyonInteractiveEvolution(IE),
102 MachadoandCardoso
i.e. the user evaluates the individuals and thus guides to succeed, i.e. produce appealing images, it would
evolution.Thereareseveralsystemsinthemusicalfield havetodoitinfewevolutionarystepsandwithalow
thatperformautomaticevaluation(e.g.[21–25]).Inthe numberofindividuals’evaluations.Ontheotherhand,
fieldofimagegeneration,however,therewasonlyone askilledusercanguidetheevolutionaryprocessinan
attempttofullyautomatefitnessassignment[26]. extremelyefficientway.She/hecanpredictwhichim-
The use of IE for image generation has achieved a agesarecompatible,detectwhentheevolutionarypro-
great popularity. The roots of these applications can cess is stuck in a local optimum, etc. In other words,
befoundinRichardDawkinsbook“TheBlindWatch- theusercanchangeitsevaluationcriteriaaccordingto
maker” [13], in which the author suggests the use of the specific context in which the evaluation is taking
a GA to evolve the morphology of virtual organisms, place.
biomorphs. This work was, apparently, the source of It is crucial to consider these idiosyncrasies in the
inspirationforthesystemsdevelopedbyK.Sims[14] developmentofanEvolutionaryArtTool.InFig.1we
and W. Latham [27], which can be considered as the showtheevolutionarymodelofNEvAr.Fromnowon,
first applications of IE in the field of the visual arts, wewilldesignatebyexperimentthesetofallpopula-
andwhichareusuallyconsideredasthemostinfluen- tionsofaparticularGPrun.
tialworksinthisarea.Thesuccessoftheseapproaches NEvAr implements a parallel evolutionary algo-
hasledtotheemergenceofanewartform,“Evolution- rithm, in the sense that we can have several different
aryArt”,andalsototheproliferationofIEapplications andindependentexperimentsrunningatthesametime.
inthisfield,usuallycalledEvolutionaryArtTools. It is also asynchronous, meaning that one experiment
Inthispaperwewillmakeanin-depthdescriptionof can be in its first population, while another can be in
anEvolutionaryArtTool,NEvAr(NeuroEvolutionary itshundredth.Additionally,wecantransferindividuals
Art).Ourobjectiveistwofold:provideusefulinforma- betweenexperiments(migration).
tiononthedevelopmentofanEvolutionaryArtTool; Theuseofmigrationallowstheconstructionofim-
presentourcurrentresearchideas,whichweconsider agedatabases.Wecan,forinstance,createanemptyex-
thatcanenrichnowadayssystems. perimentandtransfertoitindividualsthatwefindinter-
Thepaperisstructuredasfollows:inSection2we estingoruseful.Theimagesbelongingtothisdatabase
makeanoverviewofthesystem,whichcomprisesthe canbeusedtoinitialisenewexperiments,oraddedto
descriptionofNEvAr’sevolutionarymodel,usedrep- thecurrentpopulationofanexperimentwhentheuser
resentation(2.1),andgeneticoperators(2.2);wepro- finds that this addition may improve the evolutionary
ceedbypresentingexperimentalresults(Section3)and process.
the process used to produce them (3.2); in Section 4 NEvAr also allows the migration within the same
wedescribeourcurrentresearchefforts,whicharere- experiment. This feature is important due to the lim-
latedwiththedevelopmentofseedingprocedures(4.1) itedsizeofthepopulations,allowingtherevivalofim-
andautomaticevaluation(4.2),andpresentsomepre- agesfrompreviousones.Itisalsopossibletogotoa
liminary experimental results; finally, we draw some previous population and change the evaluation of the
conclusionsandpointdirectionsforfutureresearch. individuals, which is extremely useful since it allows
theexplorationofdifferentevolutionarypaths.
2. OverviewoftheSystem Throughtime,wehaveconstructedalargedatabase
of individuals. Nowadays, the extensive use of this
FitnessassignmentplaysakeyroleonECalgorithms databaseplaysakeyroleinNEvAr,aswillbeshownin
sinceitguidestheevolutionaryprocess.Consequently, Section3.Throughtheuseofthedatabasesandmigra-
thequalityoftheresultsisdeeplyconnectedwiththe tion,wetrytoovercomeoneofthemainweaknessesof
quality of the evaluation. In its present form, NEvAr ECapproaches:thelackoflongtermmemorymecha-
ismainlyanIEsystem,therefore,theuserplaysakey nisms(althoughmultiploidycanbeviewedasalimited
roleintheprocess. memory mechanism). Although the use of migration
The interaction between human and computer has anddatabasesisnotparticulartoNEvAr(e.g.systems
someadvantages,butalsoposessomeproblems.Itis, like[14,15]alsopossessthesefeatures),theemphasis
forinstance,virtuallyimpossibletouselargepopula- we give to its use is. Additionally, we have also de-
tionsizesortoperformextendedruns.Itwasclearfrom velopedautomaticseedingmechanisms,whichwillbe
the beginning of its development that if NEvAr were explainedinSection4.1.
AlltheTruthAboutNEvAr 103
Figure1. EvolutionarymodelofNEvAr.Theactiveexperimentisdepictedingrey.
Afinalwordgoestooureffortstoautomatefitness lexiconoffunctionsandterminals.Theinternalnodes
assignment.AutomaticEvaluationisstillunderdevel- are functions and the leafs terminals. We use a func-
opment; in Section 3 we will present our current ap- tion set composed, mainly, by simple functions such
proach and the experimental results achieved so far. asarithmetic,trigonometricandlogicoperations.The
The filtering module is linked with the idea of auto- terminalsetiscomposedbythevariablesx andy,and
maticevaluationandwillbeexplainedinSection4.2. byconstantswhichcanbescalarvaluesor3d-vectors.1
Theinterpretationofagenotype(anindividual)re-
2.1. Representation sults on a phenotype, which in NEvAr’s case is an
image. To generate an image, we evaluate the corre-
In NEvAr, like in most GP applications, the individ- spondingexpressionforeachpixelcoordinateandthe
uals are represented by trees. Thus, the genotype of outputisinterpretedasthegreyscalevalueofthepixel.
an individual is a symbolic expression, which can be InFig.2wepresentsomeexamplesofgenotypesand
representedbyatree.Thetreesareconstructedfroma theircorrespondingphenotypes.
Figure2. Somesimplefunctionsandthecorrespondingimages.
104 MachadoandCardoso
NEvAr allows the evolution of true-colour images, standardGPcrossoveroperator[7],whichexchanges
whichisachievedbytheuseofthe3d-vectorterminals. sub-treesbetweenindividuals.InGPmutationis,usu-
Eachofthecomponentsofthesevectorscorrespondto ally,consideredlessimportantthanrecombination[7].
a different colour channel (Red, Green and Blue or, InNEvAr,however,thepictureisquitedifferent.Con-
alternatively, Hue, Saturation and Value). We will re- ventionalGPsystemsuseasmallfunctionsetandlarge
sorttoanexampleinordertoexplainhowthecalcula- population sizes. In NEvAr this situation is inverted.
tions are performed. Consider the following expres- Therefore,mutationbecomesnecessary,inordertoal-
sion: sin(([0,1,0.5]×([1,0.5,0.5])+X). The first low the reintroduction of genetic material that would
operationtoexecuteisthemultiplicationofthetwovec- beotherwiselost.
tors;themultiplicationisnotperformedinthestandard Weresorttofivemutationoperators(seeFig.3):
way.Instead,eachofthecomponentsofthefirstvector
is multiplied by the corresponding component of the • Sub-tree swap—randomly select two mutation
second,i.e.[0×1,1×0.5,0.5×0.5]=[0,0.5,0.25]. pointsandexchangethecorrespondingsub-trees.
NextweaddthevariableX tothisvector,whichyields • Sub-tree replacement—randomly select a mutation
[0+ X,0.5+ X,0.25+ X].Finally,weapplythesin point and replace the corresponding sub-tree by a
operator to each of the components, thus obtaining: randomlycreatedone.
[sin(0+X),sin(0.5+X),sin(0.25 + X)]. By using • Nodeinsertion—randomlyselectaninsertionpoint
thisapproach,weavoidhavingtodevelopspecialop- foranew,randomlychosen,node.Ifnecessary,cre-
erators designed to manipulate vectors; instead each atetherequiredargumentsrandomly.
operator is applied to a scalar value that represents a • Nodedeletion—thedualofnodeinsertion.
colourcomponentoftheimage.2 • Nodemutation—randomlyselectanodeandchange
itsvalue.
2.2. GeneticOperators Thegeneticoperatorsinducechangesatthepheno-
type level. In Fig. 4, we show examples of the appli-
We use two kinds of genetic operators: recombina- cation of the crossover operator. As can be seen, the
tion and mutation. For the recombination, we use the crossoverbetweentwoimagescanproduceinteresting
Figure3. Examplesoftheapplicationofthemutationoperatorsonindividual A.Theindividual A(cid:7)wasgeneratedbysub-treeswap,B(cid:7)by
sub-treereplacement,C(cid:7)bynodeinsertion,D(cid:7)bynodedeletionandE(cid:7)bynodemutation.
AlltheTruthAboutNEvAr 105
Figure4. Ontheleft,thetwoprogenitorimages.Ontheright,someimagesresultingfromtheircrossover.
Figure5. Ontheleft,theoriginalimage.Ontheright,severalmutationsofthisimage.Imagea wasgeneratedthroughnodemutation,b
throughnodeinsertion,cthroughnodedeletion,dthroughsub-treeswapandebysub-treereplacement.
andunexpectedresults.Additionally,therearecasesin artistusingthetool.Inotherwords,theartistmustbe
whichtheimagesseemtobeincompatible,i.e.images abletoexpressher/himselfthroughtheuseofthetool.
that,whencombined,resultin“bad”images. TheimagesgeneratedwithNEvArduringtheearly
In Fig. 5 we give examples of images generated stagesofexperimentationwereclearlydisappointing.
through mutation. Once again, the results of this op- Thisfailuredidn’tresultfromthelackofpowerofthe
erationcangivequiteunexpectedresults. tool, but from our lack of expertise in its use. Like
any other tool, NEvAr requires a learning period. To
exploreallthepotentialofatool,theusermustknow
3. ExperimentalResults it in detail and develop or learn an appropriate work
methodology.Theresults,andusersatisfaction,depend
AsanEvolutionaryArtTool,themaingoalofNEvAr notonlyonthetoolbutalsoonitsmastering.Inother
istheproductionofartworks.Itsanalysismustbeper- words,theevaluationofapianoasatoolcanonlybe
formedwiththisinmind.Astrictlyobjectiveanalysis madebysomeonethatknowshowtoplayitwell.This
oftheresultsachievedwithNEvArisimpossible,due rises a problem: unfortunately few people know how
tothenatureoftheresults(images).Themostobvious toplayNEvAr.
waytoassesstheperformanceofthesystemistoask Due to these factors, instead of presenting charts
to a set of people to rate the images generated by the evaluatingtheperformanceofNEvAr,wewillpresent
system.Thistypeofanalysishasacertainappealand someresults,i.e.images.Ourintentionistoshowthe
appearstobeavalidwaytoevaluatethesystem’sre- type of artwork that can be produced by the system.
sults. However, this idea encompasses a fundamental Additionally,wewilldescribetheworkmethodology
error. thatwecurrentlyusetogenerateimageswithNEvAr.
NEvArisguidedbyauser.Therefore,itsgoalisto
produce images compatible with the aesthetic and/or
artistic principles of the user. As such, the evaluation 3.1. TheResults
bythirdpartiesissecondary.Thus,thereallyimportant
issueisthelevelofusersatisfaction. Nextwewillpresentseveralexamplesofimagesgen-
Itisimportanttonoticethatthefactthatatoolcan eratedwithNEvAr.TheimagespresentedinFig.6are
generate “interesting” images is irrelevant from the asubsetoftheonespresentedatthe“ArtandAesthetics
artistic point of view. What is really important is that ofArtificialLife”exhibit,thattookplaceattheCentre
theproducedartworksconveytheartisticideasofthe fortheDigitalArtsoftheUCLA,during1998.
106 MachadoandCardoso
Figure6. Someoftheimagespresentedatthe“ArtandAestheticsofArtificialLife”exhibit,curator:NicholasGesseler,1998.Furtherimages
canbefoundintheCD-ROMaccompanying[19].
Fromthepresentedexamples,itisclearthattheim- ingevolutionarypath,which,typically,correspondsto
ages generated with NEvAr are typically of abstract evolvingapromisingsetofimagesfromaninitialran-
nature.Fromthetheoretical3 pointofview,itispossi- dompopulation(generationoftheideas);inthesecond
bletoconstructpictorialimages,butinpracticethatis stage, Exploration, the “ideas” evolved on the previ-
hardtoachieve. ousstageareusedtogenerateimagesofhighaesthetic
value (exploration of the ideas); the Selection stage
3.2. TheProcess involves choosing the best produced images; the se-
lectedimages,whennecessary,willbesubjectedtoa
Thecreationofanartworkencompassesseveralstages, processofRefinement,whosegoalisthealterationof
such as: genesis of the idea, elaboration of sketches, smalldetails(i.e.itcorrespondstothefinalexecution
exploration of the idea, refinement, and artwork exe- oftheartwork).
cution.Themethodologythatweproposecanbecon- Next,wewilldescribehowweuseNEvArineach
sidered, in some way, analogous. It is composed by ofthementionedstages,referringthemostimportant
four main stages: Discovery, Exploration, Selection issuestotakeintoaccountineachofthemandtheskills
andRefinement. involved. In this description we will consider that we
Thesestagescanbedescribed,concisely,asfollows: startwitharandomlygeneratedpopulation.Theuseof
the stage of Discovery consists on finding a promis- theimagedatabasewillbedescribedafterwards.
AlltheTruthAboutNEvAr 107
3.2.1.Discovery. Ourempiricalexperienceallowsus In which concerns fitness assignment, the images
to classify the Discovery stage as the most crucial of havebydefaultafitnessvalueof0,andweusuallygive
the process, and, together with the Exploration stage, a classification greater than 0 to a very restrict set of
the one in which the faculties of the user are more images(typically1to3).Thenextpopulationwillbe,
important. therefore, composed by the combination of these im-
Discovery corresponds to the genesis of the idea, ages.Weuseroulettewheelinsteadoftournamentse-
and is therefore inappropriate to approach this stage lection.Althoughtournamentselectionisusuallypre-
withpre-conceivedideasregardingthefinalaspectof ferredinGPsystems,inouropinionrouletteselection
theartwork.Inotherwords,itisimpossibleinpractice ismoreadequatetoIEsystems,sinceitismoreintuitive
(yettempting)tothinkonanimageanduseNEvArto andallowsagreaterdegreeofcontrolbytheuser.
evolve it. This is probably the most important aspect Duringtheexplorationstage,weusehighcrossover
toretain,becauseitcontrastswithwhatisusuallyex- and mutation rates (e.g. 90% crossover and 20% mu-
pectedinatool,i.e.thatitallowstheimplementation tationprobabilities).Theobjectiveistoincreasepop-
of an idea. This aspect can be viewed as a weakness, ulation diversity, thus avoiding the loss of interest by
butitisalsothedistinguishingfeatureandstrengthof the user. In Fig. 8, we present the best images from
NEvAr (and other evolutionary art tools). A conven- population7to30.Colourwasintroducedinthe10th
tional tool only plays an important role in the artistic population. By the 21st population, the images were
processinstagesposteriortothegenerationoftheidea. sufficientlyinterestingtoallowatransitiontotheEx-
NEvAr,however,playsakeyroleinthegenerationof plorationstage.
the idea itself. Its influence is noticeable throughout
all the artistic process and in its main creative stage. 3.2.2.Exploration. Thegoalofthisstageistoexplore
The artist is no longer responsible for the creation of theideaspresentinthecurrentpopulationinorderto
the idea; instead, she/he is responsible for the recog- producesomethingclosetoanartwork.Whenwereach
nitionofpromisingconcepts.Moreprecisely,theidea thisstage,wearealreadydealingwithimagesofhigh
results from an evolutionary process, and is created aesthetic value. Through the recombination of these
by the artist and the tool, in a (hopefully) symbiotic images,weexploreaspaceofformssmallerthanthe
interaction. oneexploredinthediscoverystage,andwhichmaybe,
The most important principle that we have identi- therefore,morethoroughlysearched.
fied is the prevalence of the form in relation to the Ideally,thequalityofthepopulationsshouldincrease
colour.Thus,duringtheinitialstagesoftheevolution- steadily. In practice this is unachievable. The Explo-
aryprocesstheusershouldconcentrateintheevolved rationstagecanprolongitselfconductingtheartistto
forms and “forget”, for the time being, hers/his chro- apointthat,atleastapparently,hasnothingtodowith
matic preferences. One way to achieve independence the original one. Sometimes, the path to this point is
from colour is to use, exclusively, greyscale images relativelydirect.Therearecases,however,inwhicha
duringthefirstpopulations,untilappropriateformsare dead end is reached. In these cases, it is necessary to
found,andonlyallowthegenerationoffullcolourim- descendthehill,whichusuallyimpliesadeliberateac-
ages afterwards. This methodology proved to be ex- tionbytheuser.Thisdescentmaycauseareturntothe
tremely efficient, allowing a greater systematisation Discoverystage.
of the evolutionary process. The way NEvAr handles Apparently,thediscoveryofnewinterestingimages,
colourisadequatetothisformofoperation,sinceittyp- evenwhentheyhaveacompletelydifferentaspectfrom
icallyallowsthealterationandincorporationofcolour thepreviouslygeneratedones,isusuallyfasterthanthe
withoutsignificantlychangingtheimages’form. first discovery process. This may indicate that some
Theimagesbelongingtothefirstpopulationsusually type of learning occurs during the evolutionary pro-
fallinoneoftwoextremes,beingeithertoosimpleor cess, i.e. that, during evolution, useful combinations
too complex. The discovery stage is characterised by ofprimitivesarebuilt,andthatthesesub-treescanbe
thecombinationofsimpleimages,inordertogradually recombinedallowingthegenerationofinterestingim-
increaseimagecomplexityuntilreachinganappropri- agesinfewpopulations.
atelevel.Itisadvisabletoavoid“noisy”images,since Additionally,thehighplasticityinherenttotheused
the removal of noise is usually difficult. In Fig. 7 we representationmethodallowstheevolutionofradically
presentthefirstpopulationsofanexperiment. differentphenotypesfromresemblinggenotypes.
108 MachadoandCardoso
Figure7. Populations0,1and6ofanexperiment.Thenumbersbellowtheimagescorrespondtothefitnessgivenbytheuser.Theincreasein
thecomplexityoftheimagesisevident.Fromthe6thpopulationontheusergavepreferencetoorganicandfluidformsindetrimentofgeometric
ones.
Figure8. Thebestimages,accordingtothefitnessassignment,ofpopulations7to30(fromlefttorightandtoptobottom).
It is important to notice that an individual is more wherealreadyabandonedbytheevolutionaryprocess
thanitsphenotype.Duringevolutionthereisanaccu- (e.g. the reappearance in population 30 of an image
mulationofgeneticmaterialthatisnotexpressedinthe frompopulation5).
phenotype.Thisgeneticmaterialcanbecomeactiveat Like in the Discovery stage, the expertise of the
anytimeduetocrossoverormutation.Astrikingev- user is determinant to the success of the Exploration
idence of this fact is the reappearance of images that stage. With the accumulation of experience, the user
AlltheTruthAboutNEvAr 109
learnshowtodistinguishbetweenpromisingpathsand 3.2.3.Selection. TheSelectionstagecanbedivided
theonesthatleadnowhere,topredictwhichcombina- in two different ones: one that is concurrent with the
tionsofimagesproducebestresults,howtomanipulate evolutionaryprocess,andonethatisposterior.During
crossover and mutation rates in order to produce best thestageofExplorationthebestimages(accordingto
results,etc. the user criteria) are added to a different experiment,
InFigs.9to11wepresentsomepopulationsbelong- which works as a gallery. As stated before, NEvAr
ingtothisstage. storesallpopulations,whichallowsthereviewofthe
Figure9. TheuserisexploringimagesgeneratedduringDiscovery.Theconnectionbetweentheimagesofpopulation32andthebestimages
ofpopulations29and30isevident(seeFig.8)
Figure10. Atthisstagetheuserchoosestoabandontheideathatshe/hewasexploring.Thecircularimagesthatdominatedpreviouspopulations
whereneglected,withtheobjectiveofintroducingachangeofpath.
Figure11. Ascanbeseentherewasasignificantincreaseinpopulationdiversity.Populationsizewasincreaseinordertoallowagreater
searchwidth.Theusercontinuestoforceachangeofevolutionarypaththroughhers/hischoices,whichwillresultinadecreaseoftheaverage
qualityoftheimages,andatransitiontotheDiscoverystage.Therewhereseveralothertransitionsbetweenthisstages,andtheexploration
stagewasendedinthe57thpopulation.
110 MachadoandCardoso
evolutionaryprocessandtheadditiontothegalleryof • Discard—Imagesthatareconsideredirrelevant.
imagesthatwerepreviouslyneglected.Thisrevisionis • Useful—Images that, at least apparently, represent
highlyrecommended,andasubstantialamountoftime goodideasandthatshouldbestoredinthedatabases
shouldseparatethegenerationoftheimagesanditsre- ofNEvAr.
viewinordertoallowthenecessarydistancebetween • Refine—Imagesthatstillneedsomeworktoachieve
generationandcriticism.InFig.12,weshowtheim- thestatusofartworks.
agegalleryresultingfromtheexperimentthatwehave • Artworks—Composed,ideally,byimagesthatfully
beenusingtoillustrateourdescription. satisfytheaestheticand/orartisticcriteriaoftheuser.
Theimagesbelongingtothegallerywillbesubjected
toaprocessofanalysis,whichwillleadtotheirdivision InFig.13wepresenttheresultsoftheclassification
infourgroups: oftheimagesofFig.12intheabovementionedgroups.
Figure12. Imagegallery.
Figure13. TheUseful,RefineandArtworkgroups.Animagecanbelongtoseveralgroups.