Aging and Reverse-aging Traditional Chinese Painting Images Based on Web-Mining

New Generation Computing, 31(2013)285-309Ohmsha, Ltd. and Springer

Aging and Reverse-aging Traditional Chinese PaintingImages Based on Web-Mining

Lieu-Hen CHEN1, Meng-Feng TSAI2, Chien-Hui HSU2,Yu-Sheng CHEN1

1National Chi Nan University, No.1, University Rd., Puli, Nantou County54561, TAIWAN2National Central University, No.300, Jhongda Rd., Jhongli City, TaoyuanCounty 32001, [email protected], [email protected]@cc.ncu.edu.tw, [email protected]

Received 16 March 2013Revised manuscript received 3 September 2013

Abstract Traditional Chinese painting has a long history more thanthousands of year. No matter when these paintings are drawn, all sufferdeterioration caused both by aging and external forces. Restoring dam-aged artworks involves very professional issues beyond the scope of computerscience. However, retouching their images with aging/reverse-aging effectsthrows down a very interesting challenge to us.

Unfortunately, it is very difficult to physically simulate the optical prop-erties of aging pigments and surface material. In addition, the conventionalimage processing approaches fail to consider the above-mentioned factors, aswell as historical trends in color selection and utilization.

This paper proposes a knowledge-based and progressive refinement ap-proach to simulate and remove the aging effects in the images of traditionalChinese paintings. Web mining is an important technique which supports thisapproach by a large number of the source images. A database of informationrelated to degrees of color fade over time is constructed by collecting, catego-rizing, and analyzing the images of paintings from different eras. In addition,relationships among color distribution histograms, the different subjects be-ing depicted, the year or dynasty the painting was created, and the propertiesof different pigments are also tracked.

To separate the aging coloration influence of pigment from surface, theuntouched areas, which are intentionally left blank as a common motif in tra-ditional Chinese paintings, are utilized to provide a useful clue. The acquireddata are then modified and feedbacked into our system to progressively refine

286 L.-H. Chen, M.-F. Tsai, C-H. Hsu, Y.-S. Chen

the results. At the final rendering stage, several image processing techniquesare applied to further enhance image quality.

By using our prototype system, both aging and reverse-aging processes canbe simulated. Initial study results show that this approach has great potentialin simulating aging effects on images of traditional Chinese paintings.

Keywords: Traditional Chinese Paintings, Web-Mining, Color Analysis, ColorSimulation, Color Mapping.

§1 IntroductionTraditional Chinese painting has a long history more than thousands of

year. It often falls victim to physical and chemical changes. Restoring damagedartworks involves cleaning surface dust, filling in pigments in missing areas,patching missing pieces with glue, and replacing surface materials such as paper,canvas, and silk, but beyond the scope of computer science. However, retouchingtheir images with aging/reverse-aging effects throws down a very interestingchallenge to us.

Unfortunately, it is very difficult to physically simulate the optical prop-erties of aging pigments and surface material. In addition, conventional imageprocessing methods only consider limited information obtained from a singleimage source. Therefore, it is very difficult to realistically simulate aging andreverse-aging effects by simply using conventional image processing methods.The historical trends in color selection and utilization were not considered, too.

Previous image processing approaches developed for manipulating imagesof traditional Chinese paintings have not focused on the variation of pigments.S. C. Pei etc., implemented virtual restoration of ancient Chinese paintings usingcolor contrast enhancement and lacuna texture synthesis in an attempt to elim-inate stains and cracks.1) After that, S. C. Pei and Y. M. Chiu added saturationto the colors based on the hue of the background in order to reduce the effects ofaging and color fading.2) Later, Ayman M. T. Ahmeds method focused on colorrestoration techniques for faded photos and paintings.3)

On the other hand, there are many research works for synthesizing Non-Photorealistic Rendering images, such as water-color paintings or traditionalChinese paintings. Instead of highlighting the realism, Non-Photorealistic Ren-dering (NPR) focuses on the representation of painting styles, and it can bedivided into 2D image-based methods4) and 3D model-based methods5) accord-ing to the method of inputting object data. However, there is still no methodavailable for simulating the aging and reverse-aging phenomena.

Before solving the above problem, it is very important for us to first un-derstand the properties of color pigments used in the traditional Chinese paint-ings. Until the end of the Qing dynasty (A.C. 1875), the raw materials usedin traditional paintings could be divided into mineral pigments and plant pig-ments. The former includes white powder ( ), cinnabar ( ), vermillion( ), ochre ( ), azurite ( ) and malachite ( ), while the latter in-

Aging and Reverse-aging Traditional Chinese Painting Images Based on Web-Mining 287

cludes rouge ( ), gamboge ( ) and cyanine ( ) (Fig. 1). For samplingthe colors of these pigments, data mining was adopted to retrieve the sourceimages of the traditional Chinese paintings from internet at first. Then theseresults were verified by the procedure which we will explain below. To put itsimply, this procedure consists of a modified equation of Beer-Lambert’s Law,an association-rules model, and a progressive refinement approach.

Fig. 1 Colors of Pigments Used in Traditional Chinese Paintings

After a painting is well dry, pigments form translucent thin layers on itssurface. To describe the behavior of incident light within the thin layers, theBeer-Lamberts Law can be applied as shown in the following Equation 1.

I = I0 × exp(−αa × l) (1)

where the strength of light, I, is the strength of light incident, I0, multipliedby the exponential function exp, with an exponent of a negative absorptioncoefficient, αa, multiplied by the thickness of the medium, l.

By utilizing Equation 1, our problem is now transformed into the prob-lem where we need to determine the proper absorption coefficient, αa, for eachpigment during different time periods.

However, in the images of traditional Chinese paintings, there is anotherphenomenon needs to be addressed. As shown in related literature, the old Chi-nese artists were accustomed to spread vitriol glue onto the canvas for pigmentsto stick better before they start to paint. This glue is usually mixed with someimpurities such as gamboge. Moreover, the canvases of silk and rice-paper nat-urally turn yellow and yellow-orange with time goes by. Consequently, the colortones of old Chinese paintings tend to be too yellow.

To fix this problem, at first, the RGB color of each image pixel is trans-formed into the CMY color space, which is a subtractive color system consists ofcyan, magenta and yellow. Then the Equation 1 is modified into the followingformat Equation 2.

C0 = CI − CB × exp(−k1× saturationCIMIYI)M0 = MI −MB × exp(−k2× saturationCIMIYI)

Y0 = YI − YB × exp(−k3× saturationCIMIYI)(2)

In Equation 2, the colors of pixels, CI , MI and YI , are regarded as thestrength of the observed light. The original background color, CB , MB and YB ,are obtained from the intentionally leaving blank areas, which are resulted froma very common-used motif in traditional Chinese paintings. The thickness ofthe dried ink, l, is replaced by the saturationCIMIYI , since the saturation ishighly relative to the density of the pigments in the ink.


To reduce the influence of the background color, the background colorafter being refracted back from the canvas surface is subtracted from the ob-served color of a pixel. Its value is the original background color multiplied bythe exponential function e, whose exponents are negative absorption coefficientsmultiplied by the thicknesses of the pigment medium.

Their absorption coefficients, k1, k2 and k3, are obtained from an asso-ciation rules model of mining results, and then improved by using progressiverefinement approach which reduces the error/noise in samples. In addition, theseabsorption coefficients change over time and vary with different pigment.

Our approach also applies some image processing techniques. For exam-ple, edge detection is used to enhance the borders of the painting. The back-ground is then smoothed through our graphical user interface (GUI), and a vectorsmoothing method is applied to the whole painting to avoid any discontinuousrendering. For simulating the aging effects of newer paintings, an image-basedrendering approach is used to combine the painting with noise chosen from olderpaintings.

The above-mentioned processes are briefly illustrated in the flowchart anddataflow diagram as shown in Fig. 2.

Fig. 2 A Dataflow Diagram of Our Prototype System

The rest of this paper is organized as follows. Section II details the stepsof implementation. Section III shows the experiment results. Section IV givesour conclusions and potential future research directions.


§2 ImplementationThe approach outlined in this paper focuses on data mining of the fading

phenomena and simulates the changes of color on traditional Chinese paintings.At first, we collected source images by searching and retrieving image data fromwebsites automatically. However, there are many irrelevant images included inthe result. Therefore, for the research convenience, a preprocedure of manualfiltering is adopted in our prototype system. Additionally, the majority of thesource images used are originated from a website TELDAP (Taiwan e-Learningand Digital Archives Program, http://www.teldap.tw/en/), which contains im-ages of paintings from the National Palace Museum of Taiwan. The imagescollected ranged from the Song Dynasty (A.C.960) through the early Republicof China era, with the painting in each image classified into a 200-year timeperiod, as well as by subject. In order to provide an adequate sample pool, wecollected over 600 images of separate traditional paintings.

Traditional Chinese paintings differ from western oil paintings; they oftenleave bare large areas of the support medium, such as paper, silk, and othermaterials herein referred to as the more general term: canvas.. This emphasison empty space is a key component of artistic expression in traditional Chi-nese painting. This format leaves large areas of the painting the same colorof the canvas. Therefore, our system implements a GUI, or graphical user in-terface, whereby the canvas color can be excluded manually from subsequentcolor analysis. As a result, the canvas color was restored as pure white. Andthe fading influence of supporting material is removed from the color distribu-tion histogram of each image to a certain degree. Following Brian Smitss RGBconversion algorithm,6) pixels are converted from RGB to spectrum color spacewith wavelength values of additive primary colors (RGB) and subtractive pri-mary colors (CMY) in nano meter units. The pseudo codes of this basic processare shown in Algorithm 1. The distribution pattern of a one-dimensional colorspace for each painting is drawn by using MATLAB for research convenience.

Algorithm 1 The RGB to CMY conversion pseudo codesRequire: Color red, green and blue1: Spectrum ret = 02: if (red ≤ green) ∧ (red ≤ blue) then3: ret+ = red× whiteSpectrum4: if green ≤ blue then5: ret+ = (green− red)× cyanSpectrum6: ret+ = (blue− green)× blueSpectrum7: else8: ret+ = (blue− red)× cyanSpectrum9: ret+ = (green− blue)× greenSpectrum

10: end if11: else if (green ≤ red) ∧ (green ≤ blue) then12: ...13: else if (green ≤ red) ∧ (green ≤ blue) then14: ...15: end if16: The other two cases are similar


Color distributions are obtained by collecting and analysing web-retrievedimages of traditional Chinese paintings from the Song Dynasty to the earlyperiod of the Republic of China. We first picked landscape paintings from eachdynasty and removed their canvas colors as shown in Figs. 3 to 7. Figure 3 showsa painting from the Song Dynasty entitled Sitting beside the Stream Alone (960-1031). Figure 4 shows a painting from the Yuan Dynasty called Illustrating Rainand Shine over Spring Mountain (1299). Figure 5 is from the Ming Dynasty,named Picture Scroll of Spring Mountain (1501-1583). Figure 6 from the QingDynasty is entitled Colored Mountain and Water (1644-1728), and Fig. 7 is fromthe early Republic of China period, called Mountain and Water (1948-now).

The left sides of these figures show the original paintings, with resultsafter removal of the canvas colors displayed on the right. These results arethen processed by the steps outlined above. Figures 8(a) to 8(e) show the colordistributions of the right side of Figs. 3 to 7, and Fig. 8(f) combines these intoone graph. The result of Fig. 8 shows that early paintings tend to be reddishand late paintings tend to be more colourful.

Fig. 3 Sitting beside the Stream Alone (the Song Dynasty, 960-1031)

Fig. 4 Illustrating Rain and Shine over Spring Mountain (the YuanDynasty, 1299)


Fig. 5 Picture Scroll of Spring Mountain (the Ming Dynasty, 1501-1583).

Fig. 6 Colored Mountain and Water (the Qing Dynasty, 1644-1728)


Fig. 7 Mountain and Water (since the early period of Republic ofChina, 1948-now)

Fig. 8 A Summary of Color Distributions from Figs. 3 to 7


Additionally, according to our observation, most of the Chinese paintingartists do follow certain rules to choose pigments for given rendering targets.For example, the histograms of landscape paintings tend towards blue and green,while paintings of female subjects tend towards pink, and histograms of paintingswith flowers and birds tend to be somewhat neutral.

In generally speaking, mineral pigments used in traditional paintings tendto fade slower than botanic pigments do. Table 1 shows the names of the mainpigments and their colors and usages. Because the paintings sampled were indifferent states of preservation, different degrees of pigment fade were selectedaccording to dynasties and subject matter in which a pigment appears, from theSong Dynasty to the early Republic of China era. The results were sorted intoa fading table for each pigment as shown in Figs. 9 to 17. It should be notedthat the paintings were run through Equation 2 with the corresponding k1, k2and k3 before being included into the table.

After the images are categorized according to their age and subject, datamining was applied to generate the pigments fading rules. Data mining is the

Table 1 The Colors and Usages of the Main Pigments Used in Tradi-tional Chinese Paintings

Name Color UsageWhitepowder

Paint snow, white flowers or birds.

Cinnabar Depict flowers, birds or clothing.

Vermillion Illustrate flowers, birds, furniture and clothing.

Ochre Paint stone, tree trunks, old branches and leaves.

Ochre Represent stone, tree and moss.

Azurite Draw leaves and stone.

Rouge Illustrate flowers and make-up.

Gamboge Draw flowers and leaves.

Cyanine Illustrate stones, sky, streams, flowers, grass and clothing.

Fig. 9 The Pigment Fading Table of White Powder through SongDynasty to Republic of China

Fig. 10 The Pigment Fading Table of Cinnabar through Song Dynastyto Republic of China


Fig. 11 The Pigment Fading Table of Vermillion through Song Dy-nasty to Republic of China

Fig. 12 The Pigment Fading Table of Ochre through Song Dynasty toRepublic of China

Fig. 13 The Pigment Fading Table of Malachite through Song Dy-nasty to Republic of China.

Fig. 14 The Pigment Fading Table of Azurite through Song Dynastyto Republic of China

Fig. 15 The Pigment Fading Table of Rouge through Song Dynastyto Republic of China

Fig. 16 The Pigment Fading Table of Gamboge through Song Dynastyto Republic of China


Fig. 17 The Pigment Fading Table of Cyanine through Song Dynastyto Republic of China

process of discovering new, implicit and significant patterns from large data sets.And it is a new technology among the knowledge discovery in databases process.In this paper, we adopted association rule approach,7) which is an importantdata mining method, and applies its Apriori algorithm.8,9) Association rule min-ing appeared to analyze the market basket in 1990s. It observed the hiddenrelationships between those purchasing items and found the consuming habitof those customers in the transaction database. With Apriori algorithm, howpigments and dynasties affect the hue, saturation and brightness value (HSV)was discovered.

The association rules represent the relationships of the interested itemsor attribute values in the form A ⇒ B. It can be roughly interpreted as thelikelihood of Bs appearance when A already existed. The quality of each rule isdetermined by two probability measurements shown below.

support(A ⇒ B) = P (A⋃

B)

confidence(A ⇒ B) =P (A

⋃B)

P (A)

We usually set threshold values to trim off low quality rules from a poten-tially huge set of possible combinations. In this paper, our approach adjustedthe minimum confidence to 0.7 and the minimum support to 0.01 in a databasecontained 336 samples (a 5x5 square of pixels) for each pigment from 124 paint-ings which contains the related subjects. Parts of the strong association ruleswere listed in Table 2.

Table 2 Examples of the Strong Association Rules

Support=10%, confidence=70%ID pigments and dynasty HSV5 pigment=cyanine dynasty=Ming 336 ⇒ Saturation=15 336 conf:(1)287 pigment=cyanine dynasty=Republic of China period 224 ⇒ Saturation=55

187 conf:(0.83)52 pigment=cyanine dynasty=Song 336 ⇒ color=yellow 261 conf:(0.78)16 pigment=cyanine dynasty=Qing 336 ⇒ color=indigo 336 conf:(1)30 pigment=cyanine dynasty=Republic of China period 336 ⇒ color = indigo

336 conf:(1)407 pigment=malachite dynasty=Song 336 ⇒ Hue=90 261 conf:(0.78)408 pigment=malachite dynasty=Qing 336 ⇒ Hue=150 261 conf:(0.78)


As an example, the association rule of ID 407 is:pigment = malachite dynasty=Song 336 ⇒ Hue=90 261 conf:(0.78)This rule means that with the confidence 78%, when the pigment is mala-

chite and the dynasty is the Song Dynasty, the data entry will have 90 as itshue value (The 336 and 261 here indicate the support count of corresponding at-tribute values). According to all of mining rules, we conclude that the absorptioncoefficients of Equation 2 with k1=1.825, k2=1.85 and k3=1.875 for cinnabar,vermillion, ochre, azurite, malachite and cyanine, and k1=10, k2 = 10 and k3=10 for white powder, rouge and gamboge properly matches the trend from ourunderlying database. The calculation results of Equation 2 are displayed to usersby invoking another function of our GUI as illustrated in Fig. 18.

Fig. 18 The GUI which displays the calculation results of Equation 2

However, at the sampling stage, the observed colors of these pigmentsare mixed with background colors. It resulted in the error/noise of absorptioncoefficients.

To solve this problem, our prototype system adopts a progressive re-finement approach. The above absorption coefficients, k1=1.825, k2=1.85 andk3=1.875, are feedbacked to modify the color of each sample in the database.Then the database applied the association rule mining process again. If thestrong association rule is like pigment=xx dynasty = xx ⇒ C=xx M=xx Y=xx,Equation 2 will be filled with the values of C, M and Y , and the correspondingCI , MI , YI , CB , MB and YB , and gets the new absorption coefficients. This pro-cess was run recursively until the difference in absorption coefficients betweenthe old and the new one is small enough. The final results of the absorptioncoefficients for each pigment and dynasty are shown and illustrated in Table 3to Table 11 (the values) and Fig. 25 (the colors).

Here the values are the results obtained from the first three times of pro-gressive refinement, which tend to be convergent for the most of the selections.For example, Table 3 shows that the absorption coefficients of Azurite are con-vergent between the second and the third times.


Table 3 The results of progressively refining the absorption coefficientsof Azurite for each dynasty (in the format of values).

Azurite the SongDynasty

the YuanDynasty

the MingDynasty

the QingDynasty

the early Republicof China period

C1 1.825 1.825 1.825 1.825 1.825M1 1.85 1.85 1.85 1.85 1.85Y1 1.875 1.875 1.875 1.875 1.875C2 0.010 10.0 10.0 10.0 0.005M2 0.572 1.166 0.739 10.0 1.074Y2 2.882 1.655 1.406 1.584 2.007C3 0.010 10.0 10.0 10.0 0.005M3 0.572 1.166 0.742 10.0 1.074Y3 2.457 1.655 1.477 1.584 2.007

Table 4 The results of progressively refining the absorption coefficientsof Malachite for each dynasty (in the format of values).

Malachite the SongDynasty

the YuanDynasty

the MingDynasty

the QingDynasty


C1 1.825 1.825 1.825 1.825 1.825M1 1.85 1.85 1.85 1.85 1.85Y1 1.875 1.875 1.875 1.875 1.875C2 10.0 0.016 0.016 0.008 0.005M2 0.949 1.047 0.838 0.750 10.0Y2 3.065 10.0 1.525 0.944 1.382C3 10.0 0.016 0.016 0.008 10.0M3 0.949 1.047 0.838 0.750 0.172Y3 3.064 1.740 1.525 0.944 0.648

Table 5 The results of progressively refining the absorption coefficientsof Cinnabar for each dynasty (in the format of values).

Cinnabar the SongDynasty

the YuanDynasty

the MingDynasty

the QingDynasty


C1 1.825 1.825 1.825 1.825 1.825M1 1.85 1.85 1.85 1.85 1.85Y1 1.875 1.875 1.875 1.875 1.875C2 10.0 10.0 10.0 0.047 10.0M2 10.0 1.691 1.311 1.881 10.0Y2 1.166 10.0 2.890 2.297 10.0C3 10.0 0.004 0.003 0.047 10.0M3 10.0 1.691 1.311 1.881 10.0Y3 1.166 1.460 2.890 2.297 10.0

Table 6 The results of progressively refining the absorption coefficientsof Vermillion for each dynasty (in the format of values).

Vermillion the SongDynasty

the YuanDynasty

the MingDynasty

the QingDynasty


C1 1.825 1.825 1.825 1.825 1.825M1 1.85 1.85 1.85 1.85 1.85Y1 1.875 1.875 1.875 1.875 1.875C2 0.015 0.017 0.022 10.0 0.332M2 1.029 0.631 3.261 0.301 10.0Y2 3.814 1.296 1.547 1.631 0.659C3 0.015 10.0 0.022 0.004 0.332M3 1.029 0.535 3.261 0.301 10.0Y3 3.814 1.296 1.547 1.631 0.659


Table 7 The results of progressively refining the absorption coefficientsof Cyanine for each dynasty (in the format of values).

Cyanine the SongDynasty

the YuanDynasty

the MingDynasty

the QingDynasty


C1 1.825 1.825 1.825 1.825 1.825M1 1.85 1.85 1.85 1.85 1.85Y1 1.875 1.875 1.875 1.875 1.875C2 10.0 10.0 0.014 0.016 0.009M2 10.0 10.0 0.556 10.0 10.0Y2 10.0 1.000 2.312 1.064 10.0C3 0.007 10.0 0.014 0.016 0.009M3 1.502 10.0 0.556 10.0 0.135Y3 1.269 1.000 2.312 1.064 0.866

Table 8 The results of progressively refining the absorption coefficientsof Ochre for each dynasty (in the format of values).

Ochre the SongDynasty

the YuanDynasty

the MingDynasty

the QingDynasty


C1 1.825 1.825 1.825 1.825 1.825M1 1.85 1.85 1.85 1.85 1.85Y1 1.875 1.875 1.875 1.875 1.875C2 10.0 10.0 10.0 0.007 0.010M2 10.0 10.0 1.136 2.069 0.145Y2 1.523 1.484 1.191 3.077 0.480C3 10.0 10.0 10.0 0.007 0.010M3 10.0 10.0 1.136 2.879 0.145Y3 1.523 1.484 1.191 3.077 0.387

Table 9 The results of progressively refining the absorption coefficientsof White powder for each dynasty (in the format of values).

Whitepowder

the SongDynasty

the YuanDynasty

the MingDynasty

the QingDynasty


C1 10.0 10.0 10.0 10.0 10.0M1 10.0 10.0 10.0 10.0 10.0Y1 10.0 10.0 10.0 10.0 10.0C2 0.002 10.0 0.028 0.016 0.072M2 1.508 5.186 2.578 2.246 1.783Y2 10.0 6.697 5.341 10.0 10.0C3 10.0 10.0 0.028 0.016 0.072M3 1.508 5.186 2.578 2.246 1.783Y3 3.707 5.268 3.983 10.0 10.0

Table 10 The results of progressively refining the absorption coefficientsof Rouge for each dynasty (in the format of values).

Rouge the SongDynasty

the YuanDynasty

the MingDynasty

the QingDynasty


C1 1.825 1.825 1.825 1.825 1.825M1 1.85 1.85 1.85 1.85 1.85Y1 1.875 1.875 1.875 1.875 1.875C2 10.0 0.012 0.014 0.011 10.0M2 10.0 0.716 2.216 0.925 0.105Y2 10.0 2.680 10.0 10.0 10.0C3 10.0 0.012 0.014 0.011 10.0M3 10.0 0.716 2.216 0.925 0.105Y3 4.086 2.680 10.0 3.048 10.0


Table 11 The results of progressively refining the absorption coefficientsof Gamboge for each dynasty (in the format of values).

Gamboge the SongDynasty

the YuanDynasty

the MingDynasty

the QingDynasty


C1 1.825 1.825 1.825 1.825 1.825M1 1.85 1.85 1.85 1.85 1.85Y1 1.875 1.875 1.875 1.875 1.875C2 10.0 10.0 0.016 10.0 0.004M2 1.078 10.0 1.809 10.0 10.0Y2 10.0 10.0 10.0 10.0 1.858C3 10.0 10.0 0.016 10.0 10.0M3 1.078 10.0 2.931 0.272 10.0Y3 2.050 10.0 3.001 1.558 0.669

§3 Experiments ResultsBy utilizing the pigment fading tables, users can easily perform an aging/

reverse-aging simulation for pixels on a given painting by defining the subjectand dynasty. The results of color-mapping can be further fine-tuned interactivelythrough the GUI of our system.

Several final results of aging/reverse-aging simulation are illustrated inFigs. 19 to 21. Figure 19 shows a painting from the Ming Dynasty called TheVolume of Imitating the Ancient, while Fig. 20 shows a painting from the QingDynasty called The Volume of Ladies, and Fig. 21 is a painting from the SongDynasty called The Antithesis between the Summer Flowers and Grass. Onthe upper-left are the original paintings; on the upper-right are the results afterretouching by using a CAD tool such as PhotoImpact; and on the bottom arethe results of our approach by mapping the colors of each painting to those fromthe early Republic of China era. By comparing these two methods, all of thecontrast, brightness and saturation are adjusted to appear newer, as shown inthe middle of Figs. 19 to 21. These experiment results show that the colorssynthesized by our approach appear to be richer, brighter and more saturatedthan the colors transformed by conventional image processing methods.

Figure 22 illustrates an experiment result which simulates the aging effectfrom the Qing Dynasty to the Song Dynasty. The painting used in Fig. 22 isentitled as A Cluster of Four-Color Flowers. On the left of Fig. 22 is the imageof the original painting; to its right is the result of our approach. The colorsbecome more faded, darker and have a lower saturation than those on the left,and the background appears much rougher, as well. Additionally, the domainswhich are manually chosen by users in our GUI, such as the grass-green andpurple areas, are not included as the blended colors of pigments.

Because this approach is based on a very wide range of web-mined images,the sizes of these paintings differ from one another, and their resolutions aregenerally low. There are also distortions which can appear on the images afterprocessing, like those shown in Fig. 23, which shows a painting from the SongDynasty called Cherry and Yellow Sparrow, measuring 514 × 534 pixels. Onthe left is the original painting; to its right is the simulation result which failedto map all of the colors to the proper states in the early Republic of China


Fig. 19 Shows a Painting from the Ming Dynasty called The Volumeof Imitating the Ancient


Fig. 20 Shows a Painting from the Qing Dynasty called The Volume of Ladies


Fig. 21 A Painting from the Song Dynasty called The Antithesis be-tween the Summer Flowers and Grass


Fig. 22 Illustrate a painting from the Qing Dynasty called A Clusterof Four-Color Flowers. On the left is the original painting; toits right is our aging result, which simulating the changes ofcolors as if the painting was drawn in the Song Dynasty.

Fig. 23 Shows a painting from the Song Dynasty called Cherry andYellow Sparrow, measuring 514 × 534 pixels. On the left isthe original painting; to its right is the simulated result whichfailed to map all of the colors to their states in the early Re-public of China era.

era. Furthermore, due to the low resolution of the image, the colors becomediscontinuous after being processed. This problem can be solved by carefullychoosing images with higher resolution at the sampling stage and verifying thecolors of each pigment by the following procedure.

After the absorption coefficients for each pigment were found, the resultsof web mining were verified by the procedure which gives a comparison betweenthe colors averaged from internet and the colors scanned from the album oftraditional Chinese paintings. It should be noted that these colors were processedthrough Equation 2 with the corresponding k1, k2 and k3 before being compared.The differences between them are illustrated in Fig. 24. The columns show thename of the pigments, their supposed colors, the number of the images collected


from internet, their color averaged from these images, their color scanned fromthe albums, and the difference between the colors of the above two columns,respectively. To calculate this difference, the ranges of the two colors wererecorded in HSV color space, and their intersection divided by their union. Weaveraged these results and got the difference. Because the backgrounds tend tobecome yellow, there are more differences related to pigments using azurite andcyanine. As a result, we conclude that most of the images of paintings collectedfrom websites are still reliable, and the visual inaccuracy of simulated colors isacceptable.

As the GUI asks users for manual inputs, user’s knowledge to the tradi-tional painting techniques and related pigment changes can be helpful for reduc-ing the iteration times of progressive refinement processes. This user familiaritywith the subject material and data-driven technical application can further en-hance the precision of our approach.

Fig. 24 The pigments original colors, scanned colors, and the averagedcolors of images collected from the internet.


Fig. 25 The results of progressively refining the absorption coefficientsfor each pigment and dynasty (illustrated in colors).

§4 ConclusionIn this paper, we propose a technique to analyze the aging and reverse-

aging phenomena of pigments in traditional Chinese paintings across severaldynasties. This research goal is successfully achieved by adopting several tech-niques including: web mining to discover the source images, modeling and pro-cessing colors by the Beer-Lamberts Law, extracting the fading association rulesfor each pigment and dynasty, and finally simulating the color fading effects withdifferent degrees based on heuristic formulas.

The pigments of traditional Chinese paintings have become much morediverse since the Song Dynasty. Changes in chemical compositions influencecolor and fading progress, especially after the introduction of western-createdpigments came into usage from the end of Qing Dynasty (1875). Therefore,the painting samples from the later Republic of China era are not adopted, andmodern pigments in traditional Chinese paintings are not simulated, because thecolors of pigments and their components differ greatly from those used in thepast.

As one of the future works, extracting additional information regardingtraditional Chinese paintings, such as the painting styles of artists, the chemicalproperties of pigments, and so on, would further enhance our knowledge base.Better sampling strategies and mining methods will increase the simulation pre-cision. Experts in traditional Chinese paintings could be consulted as to whetherthe simulated results created by our system are reasonable or not. We thereforewelcome any advice and recommendations for later approaches.

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Lieu-Hen Chen, Ph.D.: He is currently an associate professor of

the Department of Computer Science and Information Engineer-

ing at National Chi Nan University, Taiwan. His research inter-

ests include Computer Graphics, Information Visualization, and

Digital Arts. He received a B.S. in Computer Science and Infor-

mation Engineering from National Taiwan University, and M.S.

and Ph.D. degrees in Electrical and Electronic Engineering from

the University of Tokyo.

Meng-Feng Tsai, Ph.D.: He is currently an assistant professor of

the Department of Computer Science and Information Engineer-

ing at National Central University, Taiwan. His research inter-

ests include Data-warehousing, Database, Data-Mining, Aggrega-

tion Computing, Distributive System, Bio-informatics, Decision-

support systems. He received B.S. and M.S. in Computer Science

and Information Engineering from National Taiwan University,

and Ph.D. degrees in Computer Science department from the

University of California.


Chien-Hui Hsu: She is a graduate student in the Department of

Computer Science and Information Engineering at National Cen-

tral University. Her research interests include Computer Graph

and Data Mining. Contact her at [email protected].

Yu-Sheng Chen: He is a Ph.D. student in the Department of

Computer Science and Information Engineering at National Chi

Nan University. His research interests include Level of Detail,

Computer Animation, and Virtual Environment. Contact him at

[email protected].

Aging and Reverse-aging Traditional Chinese Painting Images Based on Web-Mining

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