Large-Scale Quantitative Analysis of Painting Arts (CABDyN, University of Oxford, 2013)

Large-Scale Quantitative Analysis of Painting Arts

, Dongwoo Kim2, Seung-Woo Son

3, Alice Oh

2 , Hawoong

Jeong1,4

1 Department of Physics, KAIST, Republic of Korea2 Department of Computer Science, KAIST, Republic of Korea

3 Department of applied physics, Hanyang University, Republic of Korea4 Institute for the BioCentury, KAIST, Republic of Korea

Daniel Kim 1

PaintingStyle

Introduc-tion

Sum-mary

Color Spec-trum

Color Palette

CountingFre-

quency

FractalAnalysis

SurfaceAnalysis

Colorful Dots

Colorful Lines

Colorful Sur-faces

Stylometry

Litera-ture

Music

Period # imagesMedieval 331Early Renaissance 995Northern Renais-sance

1,047

High Renaissance 677Mannerism 911Baroque 3287Rococo 360Neoclassicism 163Romanticism 663Realism 146Total 8798

Data set 1: Web Gallery of Art (2009)

* Source: http://www.wga.hu/

Note * Title: Schloss Wilhelmshöhe with the Habichtswald c. 1800 Oil on canvas Neue Galerie, Kassel * Artist: German painter, Johann Erdmann Hummel (11 September 1769, Kassel — 26 Oc-tober 1852, Berlin)

P(r

)10-2

10-3

10-4

10-5

10-6

100 101 102 103 104 105

Rank: r

Null model

100 101 102 103 104 105 106

Rank: r

10-2

10-4

10-6

10-8

10-10

MedievalEarly Renaissance

Northern Renais-sanceHigh Renais-sanceMannerism

BaroqueRococo

NeoclassicismRomanticism

Realismphoto

P(r

)

PaintingStyle

Introduc-tion

Sum-mary

Color Usage

Color Palette

Count-ingFre-quency

FractalAnalysis

SurfaceAnalysis

Measuring“fractal dimen-sion“in RGB color space

)/(log

)(loglim)(

10

NSdbox

Blue

Red

Green

Measuring“fractal dimen-sion“in RGB color space

Blue

Red

Green

Th

e n

um

ber

of

non

-em

pty

boxes

Box side length

106

105

104

103

102

100 101 102

Box-c

ou

nti

ng

di-

men

sio

n

3

2.8

2.6

2.4

2.2

2Medieval

Early Renaissance

Northern Renais-

sance

High Renaissance

Mannerism

Baroque

Rococo

Neoclassicism

Romanti-cism

Real-ism

Period

Art historical considera-tion

In the medieval age …

1. Specific rare pigments were pre-ferred.

2. There was no physical mixing tech-nique.

Since the Renaissance…

1. The oil colors and the color mixing technique.

2. New painting techniques such as Chiaroscuro, Sfumato, Cangiante, …

PaintingStyle

Introduc-tion

Sum-mary

Color Usage

Color Palette

CountingFre-

quency

FractalAnalysis

SurfaceAnalysis

Two Painting Styles: Chiaroscuro & Sfu-mato

Painting Style 1: Chiaroscuro

↓Light ↓

Dark

Chiaroscuro ↓

Light ↓Dark

Brightness difference• Emphasis• Contrast• Perspective

Measuring“surface rough-ness"

Rough-ness ex-ponent

Brightness

Y-axis

2h(x)r)h(xG(r)

2r~

255

0

200400600

800

0

200400

600800

1000X-axis

0Note * Title: “St John the Evangelist Drinking from the Poi-soned Cup” * Artist: Italian painter Taddeo Gaddi (1348-1353)

r

104

103G

(r)

102

100

101

102

103

G(r)~ r2×0.28

Brightness

Y-axis

255

0

200400600

800

0

200400

600800

1000X-axis


Measuring“surface rough-ness"

0.40

0.35

0.309

0.25

0.20

〈α〉

Medieval

Early Renaissance

High Renaissance

Mannerism

Baroque

Rococo

Neoclassicism

Romanti-cism

Real-ism

Period

Northern Renais-

sance

Two examples of chiaroscuro

• Jackson Pollock

• Louis Wain

Jackson Pollock (1912-1956)

Note* Title: “Number 20, 1948, 1948”* Artist: American painter, Jackson Pollock (1912-1956)

Brightness

Y-axis

Brightness

X-axisY-axis

r

104

103

102

100 101 102 103

2α ~0.28 G

(r)

r100 101 102 103

101

102

103

104

105

G(r

)

2α ~0.008

255

0

400

800

0

400800

0

X-axis400

800

0

255

0

200

600

Louis Wain (1860-1939)

α0.30

0.25

0.20

0.15

0.10

0.050

Painting Style 2: Sfumato

Shading around eyes

Sfumato

Measuring“image en-tropy"

Brightness

Y-axis

255

0

200400600

800

0

200400

600800

1000X-axis


x )x(m

)x(plog)x(pH

p(x)=h(x)/S, m(x)=1+σ2(x)

y y

yhyhx

2

2 )(9

1)(

9

1)(

<

Examiningcharacteristics of contracted im-ages

Radius ∝ color usage ~ mass

Red

Gre

en

4080

0

120

60

120

20

80

140

0

BlueCenter of mass

← Fixed point (FP)

↑ Shuffled Fixed point (SFP)

Shuffled image

↓

Blue

Red

Gre

en

4080

0

120

60

120

20

80

140

0

Medieval

Early Renaissance

High Renaissance

Mannerism

Baroque

Rococo

Neoclassicism

Romanti-cism

Real-ism

Period

Northern Renais-

sance

6

10

14

18

22

Submitted work

Ongoing work


Date # im-ages

1051-1100 311101-1150 221151-1200 201201-1250 351251-1300 1141301-1350 8041351-1400 2971401-1450 13281451-1500 31801501-1550 37351551-1600 19271601-1650 37531651-1700 22281701-1750 13421751-1800 9261801-1850 11451851-1900 1893Total 22780



Date# im-ages

genre

histori-cal

inte-rior

land-scape

mythologi-cal

other

por-trait

reli-gious

still-life

study

1051-1100

31 0 0 0 0 0 0 0 31 0 0

1101-1150

22 0 0 0 0 0 2 0 20 0 0

1151-1200

20 0 0 0 0 0 1 0 19 0 0

1201-1250

35 0 0 0 0 0 2 0 33 0 0

1251-1300

114 0 0 1 0 0 0 0 113 0 0

1301-1350

804 0 1 15 4 22 7 3 752 0 0

1351-1400

297 0 3 8 0 2 1 4 278 0 1

1401-1450

1328 0 10 8 2 15 23 83 1187 0 0

1451-1500

3180 9 58 49 14 161 38 257 2590 3 1

1501-1550

3735 103 88 28 75 392 74 655 2313 6 1

1551-1600

1927 77 47 43 70 271 27 319 1029 42 2

1601-1650

3753 360 84 68 514 494 35 788 1011 379 20

1651-1700

2228 566 52 87 485 177 24 235 328 271 3

1701-1750

1342 243 41 15 279 203 10 240 216 92 3

1751-1800

926 141 42 20 241 61 18 282 89 28 4

1801-1850

1145 102 133 18 289 81 121 317 59 18 7

1851-1900

1893 323 33 16 757 50 156 407 27 105 19

Total 22780 1924 592 376 2730 1929 539 3590 10095 944 61


Paintings are classified!

Data set 3: Google art project (2013)

* Source: http://www.google.com/culturalinstitute/project/art-project

-2550-2535-2520-2504-2489-2474-2459-2444-2428-2413-2398-2383-2367-2352-2337-2322-2307-2291-2276-2261-2246-2231-2215-2200-2185-2170-2155-2139-2124-2109-2094-2078-2063-2048-2033-2018-2002-1987-1972-1957-1942-1926-1911-1896-1881-1866-1850-1835-1820-1805-1790-1774-1759-1744-1729-1713-1698-1683-1668-1653-1637-1622-1607-1592-1577-1561-1546-1531-1516-1501-1485-1470-1455-1440-1424-1409-1394-1379-1364-1348-1333-1318-1303-1288-1272-1257-1242-1227-1212-1196-1181-1166-1151-1135-1120-1105-1090-1075-1059-1044-1029-1014-999-983-968-953-938-923-907-892-877-862-846-831-816-801-786-770-755-740-725-710-694-679-664-649-634-618-603-588-573-557-542-527-512-497-481-466-451-436-421-405-390-375-360-345-329-314-299-284-269-253-238-223-208-192-177-162-147-132-116-101-86-71-56-40-25-1052036516681971121271421571731882032182332492642792943093253403553703864014164314464624774925075225385535685835986146296446596756907057207357517667817968118278428578728879039189339489649799941009102410401055107010851100111611311146116111761192120712221237125312681283129813131329134413591374138914051420143514501465148114961511152615411557157215871602161816331648166316781694170917241739175417701785180018151830184618611876189119071922193719521967198319981

10

100

1000

10000

Year

# im

ag

es

Covering over 4000 years!


979

994

1009

1024

1040

1055

1070

1085

1100

1116

1131

1146

1161

1176

1192

1207

1222

1237

1253

1268

1283

1298

1313

1329

1344

1359

1374

1389

1405

1420

1435

1450

1465

1481

1496

1511

1526

1541

1557

1572

1587

1602

1618

1633

1648

1663

1678

1694

1709

1724

1739

1754

1770

1785

1800

1815

1830

1846

1861

1876

1891

1907

1922

1937

1952

1967

1983

1998

1

10

100

1000

10000

Year

# im

ag

es

32808 paintings !



979

994

1009

1024

1040

1055

1070

1085

1100

1116

1131

1146

1161

1176

1192

1207

1222

1237

1253

1268

1283

1298

1313

1329

1344

1359

1374

1389

1405

1420

1435

1450

1465

1481

1496

1511

1526

1541

1557

1572

1587

1602

1618

1633

1648

1663

1678

1694

1709

1724

1739

1754

1770

1785

1800

1815

1830

1846

1861

1876

1891

1907

1922

1937

1952

1967

1983

1998

1

10

100

1000

10000

Year

# im

ag

es

Dataset 2 + Dataset 3→ Over 55,000 paintings !


• Roughness exponents of “Web Gallery of Art images” (α vs. Year)

• Radius of gyration in RGB color space

What we are studying these days…

• If we iteratively shuffle an image until being a noisy image, how much time is required for each image?

• How many persons are appeared in paint-ing arts?

• How can we classify paintings by artists?

• Time evolution of roughness exponent in a video database.

Future work

Informaion Measure Represen-tation

Color types Fractal analysisColor palette diversity

Color types+ Color usage

Radius of gyrationColor palette diversity

Color types + Color usage+ Spatial correlation

Rescaling analysis Orderness

Spatial correlation Surface roughnessOrderness, painting style

Local spatial correla-tion Image entropy painting style

Sum-mary

Sum-mary• Massive painting databases are analyzed.

• Paintings and photos can be statistically dis-tinguished by rank-ordered color usage distri-bution shape.

• The changes of fractal dimension with the times can be historically interpreted as color palette expansion.

• Increasing trend of brightness surface rough-ness can be described as the art historical ren-ovation of painting technique and the diversifi-cation of painting genre.

• Jackson Pollock’s drip paintings are close to random images compared to European paint-ings based on roughness exponent values

Thank you!

Large-Scale Quantitative Analysis of Painting Arts (CABDyN, University of Oxford, 2013)

Data & Analytics

Transcript of Large-Scale Quantitative Analysis of Painting Arts (CABDyN, University of Oxford, 2013)