Pictures at an Exhibition

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1 1 Pctures at an Exhbton Stephane Kwan and Karen Zhu Department of Electrcal Engneerng Stanford Unversty, Stanford, CA 9405 Emal: {skwan1, Abstract An mage processng algorthm s desgned and mplemented to dentfy pctures at an exhbton. The algorthm frst performs segmentaton to obtan the pantng n the nput mage by lookng at both the lumnance and chromnance components of the nput mage. The lumnance ratos and color hstograms are computed to dentfy the pantng accordng to a set of pre-calculated statstcs. The algorthm s able to dentfy mages taken from dfferent angle and dfferent llumnaton condtons. The algorthm correctly dentfes all mages n a gven tranng set. The average speed of the algorthm s 20-0 seconds per nput mage. A. TRODUCTO DVACES n moble magng technology has enabled many nterestng applcatons of hand-held moble devces. One example s moble augmented realty. By amng a hand-held devce at an obect of nterest, the user can get nformaton regardng that obect from a remote database. Ths applcaton allows hand-held devces to serve as electronc museum gudes. For example, the user can use a camera phone to take a snapshot of a pantng of nterest, and lsten to commentares about the pantng. One of the crtcal components of such applcaton s the mage processng algorthm used to recognze the pantng from the pcture taken. How the snapshots of the pantngs look depend on camera angle, llumnaton and many other factors. Therefore, a combnaton of dfferent mage processng technques s requred to guarantee successful recognton of the pantngs. n ths proect, we developed a fast and robust algorthm for recognzng pantngs n the European Gallery of the Cantor Arts Center. The nput of our system s an RGB mage of a pantng from the European Gallery, and the output s the ttle of the pantng. We are also gven a set of tranng mages, so that we can tran our algorthm to set up approprate parameters and threshold before actual testng. We also tested the segmentaton part of our algorthm wth other test mages that we generated.. MAGE PROCESSG ALGORTHM Our algorthm conssts of three man stages. A) Segmentaton of nput RGB mage. B) Extracton of lumnance and color nformaton of the pantng. C) dentfcaton of Pantng. We frst segment the nput JPEG mage to obtan the part that only contans the pantng. We then dvde the lumnance component of the nput mage nto four quadrants, and obtan statstcs of the spatal dstrbuton of the lumnance component of the nput mage. We also computed the color hstograms of the R, G and B color components. Fnally, from the nformaton obtaned from the mage, namely the heght to wdth rato, lumnance statstcs and color hstograms, we fnd the best match from the pre-calculated pantng statstcs n our database, and dentfy the nput pantng to be the best matched pantng n the database. A. Segmentaton The obectve of the segmentaton process s to dentfy the part of the pcture that contans the pantng. We try to create a bnary mask of the nput mage, where the pantng regon s labeled as 1, and the other regon labeled as 0. Among the pantngs that the proect tres to dentfy, the pantng frames are black, brown or golden. Black frames have lumnance values lower than the overall mean of the lumnance component of the mage, whle brown and golden frames have Cb values lower than the overall mean of the Cb component, but Cr values hgher than the overall mean of the Cr component of the mage. Usng these propertes, we transform the RGB nput mage to the YCrCb doman, and bnarze the Y, Cr and Cb components of the mage, settng potental frame regons to 1, and non-frame regons to 0. From these three resultng bnary mages, we create two temporary bnary masks, one from the bnarzed lumnance mage (LuMas, and the other from combnng the Cr and Cb bnary mages (CrCbMas. The choce of one mask over the other s hghly dependent on the mage under test. Sometmes, the lumnance component dentfes the pantng regon more accurately, whle other tmes, the Cr and Cb components dentfy the pantng regon better. Fg. 2 shows the LuMask and Fg. shows the CrCbMask for one of the tranng mages. To make sure the entre pantng, but not only the frame are labeled as 1, we fll up holes n connected regons of 1s n the two masks. Then, we label the connected regons n the masks, and dentfy the largest regon. For each of the masks, base on the crtera that the maorty of the dentfed regon has to be labeled as 1 n the mask, we dentfy the largest regon that fulflls the crtera to be the pantng regon. We then set the pxels n the respectve dentfed pantng regons to 1, and other regon to 0. As the next step, we choose to use one of the masks by

2 2 determnng whether LuMask or CrCbMask gves a better estmate of the pantng regon base on crterons lsted below: 1) We know that the pantng has to be located at the center of the nput mage. Therefore, f one of the masks does not contan the center pxel, then the other mask s chosen. 2) Snce the pantng cannot resde at the corner of the nput mage, f at least one of the corners of one of the masks s labeled as part of the pantng, the other mask s used. ) f the heght of LuMask s shorter than CrCbMask, we choose the LuMask. The ratonal behnd s that the CrCbMask s usually shorter than the LuMask snce the LuMask sometmes ncludes shadows. However, f the CrCbMask s longer, ths means that t captured a lot of unnecessary parts ether at the top or bottom of the pantng, and therefore we do not want to use ths mask. 4) f the overlappng area of the dentfed pantng regons n the two masks s greater than 90% of the total dentfed pantng regon, then we choose the mask that gves a smaller panng regon. Snce the two masks are very smlar, and t s lkely that the smaller mask ncludes less unwanted regons besdes the pantng. However, f the overlappng area s less than 90%, then most lkely the smaller mask faled to capture the entre pantng. Thus, the larger mask s chosen. After choosng the mask, the mask s further refned to elmnate unwanted regons at the sdes of the dentfed pantng area. Fg. 4 shows the fnal mask of one of our tranng mages. The heght to wdth rato (HWR) of the pantng s also recorded at the end of ths process. Fnally, we overlay the mask wth the orgnal RGB mage, and extract the segmented pantng from the RGB mage. From ths pont onwards, we only work wth the segmented part of the mage. Ths reduces the sze of the mage we have to work wth, and speeds up the processng tme requred. Fg. 5 shows an example of a segmented pantng. Fg. 2. LuMask of The Panter. Fg.. CrCbMask of The Panter. Fg. 4. Fnal mask of The Panter. Fg. 1. Orgnal mage of The Panter.

3 TL TR BL BR Fg. 5. Fnal segmented mage of The Panter B. Extracton of Lumnance and Color nformaton The goal of extractng lumnance and color nformaton from the pantng s to collect relable statstcs about the pantng, so that t can be compared to the pre-calculated statstcs, and dentfy the nput pantng. The man reason for extractng lumnance and color nformaton from the pantng s that n a museum settng, lghtng s controlled. Therefore, by strategcally processng lumnance and color statstcs, varaton of these statstcs of the same pantng due to factors such as dfferent camera angles and dstance from pantng wll be smaller than varaton between dfferent pantngs. Hence we wll be able to dentfy the correct pantng usng these statstcs. B1. Lumnance Rato We separate the lumnance component of the segmented mage nto four quadrants: top left (TL), top rght (TR), bottom left (BL), and bottom rght (BR) as shown n Fg.6. Then, we calculate sx lumnance ratos of the lumnance mage: Fg. 6. llustraton of the four quadrants for lumnance rato calculaton. 1) mean(tl+tr) /mean (BL+BR) (TBR) 2) mean(tl + BL)/mean(TR + LR) (LRR) ) mean(tl)/mean(entre mage) (Q1R) 4) mean(tr)/mean(entre mage) (Q2R) 5) mean(bl)/mean(entre mage) (QR) 6) mean(br)/mean(entre mage) (Q4R) Although the absolute lumnance values of the overall segmented mage may vary sgnfcantly dependng on how the pcture was taken, provded that the pcture s not taken at an acute angle, the sx ratos we collect wll reman smlar snce lghtng s controlled. For nstance, the TBR of a pantng that has a brght top and dark bottom wll always be greater than one no matter how the pcture s taken. These ratos gve spatal nformaton about the pantng. B2. Color Hstogram Color n an mage convey a lot of nformaton and s often used to ad computer vson applcatons. The color hstogram method has been frst proposed by Swan and Ballard n [1]. Obects can be dentfed by matchng the color hstograms of each of the color channels between dfferent mages. The drawback of ths approach s that any slght varaton n lghtng condton can sgnfcantly nfluence the shape of the color hstogram and weaken ts robustness [2]. The Comprehensve Color mage ormalzaton method s therefore suggested by Fnlayson et al. n [] to dscount for lghtng geometry and llumnant color together at the same tme. Ths method utlzes an teratve approach, normalzng

4 4 lghtng geometry n one step and llumnant color n another, untl each color channel stablzes n ts values. ( ),, k 1, k R (1) C ),, ( (2) k 0 k, n equaton (1) and (2), s the * mage matrx of red, green, and blue values, wth each color channel havng values. R() s the row-normalzaton step to dscount for lghtng geometry. C() s the column-normalzaton step to dscount for llumnant color. t has been proven that ths process always converges, the convergent mage s unque and the convergence s very fast, n only four to fve teratons. [] n our mplementaton, we frst apply Comprehensve Color ormalzaton to the segmented mages by performng fve teratons of a two step process. n step 1, we normalze each pxel value for lghtng geometry by applyng equaton (1). n step 2, we normalze each pxel value for llumnant color by applyng equaton (2). Ths normalzaton process put the pxel values n the range of 0 to 1. Step1: r, g, b,, r g b r g b r g b Step 2: r, g, b r r 1, r r mean( R) 1 r, g, b,, g g, g g meang ( ) b 1 b b b mean( B) After normalzaton, we compute the color hstogram for each of the color channels. The hstograms are generated by usng 256 bns of equal wdth n the range of 0 to 1. Fnally, we normalze the hstogram by dvdng each bn n the hstogram wth the total number of pxels n the segmented mage to account for dfferent szes of mages. Three color hstograms are saved for each segmented mage for dentfcaton of the pantng. C. dentfcaton of Pantng After collectng all the necessary statstcs from the prevous stage, we proceed to comparng these data wth statstcs we pre-calculated about the pantngs we have to dentfy. n order to reduce the number of false postves, the algorthm frst attempts to elmnate as many mprobable pantngs as possble before choosng the most probable pantng n the last stage of the dentfcaton process. C1. Pre-Calculaton of Reference Lumnance Ratos and Color Hstograms from Tranng Set Reference lumnance ratos and color hstograms for each pantng are calculated and stored n our database. We compare the lumnance ratos and color hstograms of our nput test mage to these reference data to determne whch pantng the nput mage belongs to. To get the sx reference lumnance ratos and three reference color hstograms for each pantng, frst we run our segmentaton algorthm on each of the 99 mages n the tranng set, and calculate the lumnance ratos and color hstograms for each of these mages. Then, we group the mages accordng to the pantng they belong to (e. groups, each wth three mages), and take the average lumnance ratos and color hstograms of the three mages n the group to be the reference lumnance ratos and hstograms for the pantng. C2. Pre-Calculaton of Lumnance Ratos Thresholds for dentfyng Pantngs To elmnate pantngs that are defnte msmatches wth our nput test mage n the lumnance rato test, we have to set thresholds that represent the maxmum varatons allowed for lumnance ratos between pctures of the same pantng. n total, we need sx threshold values. To fnd the thresholds, we frst dvde our tranng set (99 mages) nto three groups, where each contans mages, wth one mage from each pantng. We then do three teratons of tranng and testng as descrbed below. n each teraton, we use two groups of mages as our tranng set, and the last group as our test set. The three teratons are 1. Tranng = Group 1,2, Testng = Group 2. Tranng = Group 1,, Testng = Group 2. Tranng = Group 2,, Testng = Group 1 For each teraton, we frst create our reference lumnance ratos R LumnanceRato usng the same method n C1. However, nstead of takng the average of three mages, we only take the average of the two mages n the tranng set for ths current teraton. Then, we compare the lumnance ratos of mage () n our test set wth the correspondng reference statstcs R LumnanceRato () of the same pantng we obtaned n ths teraton usng the equatons (), Test LuRatoVaraton(, Lu mn ancerato R (, R Lu mn ancerato Lu mn ancerato (, where {1...}, representng the pantng number, and k {1...6}, representng one of the sx lumnance ratos. We then record the maxmum varaton for each lumnance rato among the pantngs n each teraton. (, ()

5 5 After the three teratons, we get three sets of maxmum lumnance ratos varatons. For each lumnance rato, we then take the maxmum value of the three sets, further ncrease these maxmum values by a small amount, and use these numbers as the threshold values that we need. The reason for separatng our ntal tranng set nto three groups and obtan thresholds as descrbed above s two folded. Frst, f we use all three mages of the same pantng to set our reference, and obtan the thresholds by calculatng the maxmum varatons of the same three mages wth the reference, these thresholds are not lkely to be robust. The statstcs of our test mages are part of the reference. By separatng the tranng set and test set, statstcs of the reference and the test data are ndependent, thus the thresholds set usng ths method are lkely to be more robust. Second, varatons observed by averagng two pctures and testng on the thrd pcture are lkely to be larger than varatons observed by averagng three pctures and testng wth a forth one. Therefore, by settng the thresholds to be the maxmum varatons from the reference found by usng two mages per pantng, the thresholds should be large enough to ncorporate varatons from formng the reference usng three mages for each pantng, and testng wth a forth one. C. dentfyng the Pantng As a frst step of elmnatng unlkely pantngs, we look at the heght to wdth rato (HWR) of the nput pantng. f the HWR of our nput pantng s less than 1, then we elmnate all the pantngs n the tranng set that have HWR greater than 1. On the other hand, f the HWR s greater than 1.8, then we elmnate all the pantngs that have HWR less than 1. Unlke the prevous case where the HWR threshold s 1, we set the HWR threshold to be 1.8 n ths case to avod false postve conclusons. When a pcture of a pantng s taken at an angle, t s possble that the heght of the pantng n the segmented pcture s greater then ts wdth, although the wdth of the pantng s actually greater than ts heght n realty. Under normal vewng condtons of pantngs n a museum, ths dstorton caused by vewng angle only happens when the length of heght s relatvely close to the length of wdth. Therefore, f HWR s greater than 1.8, we can be sure that the pantng under test must have ts heght greater than ts wdth. n the second step of the dentfcaton algorthm, we calculate the varaton of the 6 lumnance ratos of the nput pantng from the pre-calculated reference lumnance ratos of the pantngs. Of the possble canddate pantngs, we only perform the sx lumnance ratos varaton calculaton f they have not been elmnated n the frst step. The lumnance ratos varatons are calculated usng equaton (), and the results are compared to the pre-set threshold. We elmnate the pantng f more than one of these lumnance ratos exceeds ther correspondng thresholds. For pantngs that are not elmnated, we store the mean of the resultng lumnance ratos varatons, and call t LuVar. LuVar s computed usng equaton (4), LuVar( ) mean( LuRatoVaraton(, Threshold( ) (4) where {1...}, representng the reference pantng number, and k {1...6}, representng one of the 6 lumnance ratos. The larger the LuVar s, the better match the nput test mage s to the reference pantng. ote that we only nclude LuRatoVaraton(, that s smaller than the threshold. So whle there are sx lumnance ratos, f one of the ratos s above the threshold, then we only take the average of the other fve lumnance rato varatons. n the thrd step, we compare the color hstograms of the nput pantng wth the reference color hstograms of the left over canddate pantngs. The varaton of the color hstograms of the nput pantng from the reference pantng s calculated usng equaton (5), ColorHstogramVaraton() meantestred _ hstogram( ) RRED _ hstogram( ) + meantestgree _ hstogram( ) RGREE _ hstogram( ) + meantest BLUE _ hstogram ( ) RBLUE _ hstogram ( ) (5) where {1...}, representng the reference pantng number. The smaller the ColorHstogramVaraton, the better match the nput test mage s to the reference pantng. Fnally, from the remanng reference pantng choces, we form a comparson metrc by dvdng LuVar values wth ColorHstograVaraton values. LuVar( ) ComparsonMetrc( ) (6) ColorHstogramVaraton( ) We dentfy the nput test mage as the reference pantng that gves the largest ComparsonMetrc value.. RESULTS We ran our algorthm on the gven 99 tranng mages, and all of them were recognzed accurately. The average processng tme for each nput mage s 20-0 seconds. V. COCLUSO We have desgned and mplemented a fast and robust algorthm for gallery pantng dentfcaton. The robustness s acheved by a combnaton of dfferent mage processng methods. By takng nto account both lumnance and chromnance nformaton of the nput mage n creatng the mask, segmentaton can successfully elmnate unwanted mage areas. Heght to wdth rato, lumnance rato, and color hstogram each focuses on a dfferent characterstc of a partcular pantng. Together they extract crtcal nformaton from the pantngs, and dfferentate them effectvely. The speed of our algorthm s due to ts smplcty. Each of the mage processng method s very smple and requres relatvely lttle computaton power.

6 6 V. DVSO OF WORK The followng s a rough breakdown of work for the proect: Stephane: - Segmentaton Algorthm - Lumnance Rato calculaton - dentfcaton of Pantng - Report Karen: - Color Hstogram calculaton - Generate new test set and testng - Report V. REFERECES [1] M. J. Swan and D. H. Ballard, Color ndexng, nternatonal Journal of Computer Vson, 7(1):11 2, [2] B. Schele and J. L. Crowley, Recognton wthout Correspondence usng Multdmensonal Receptve Feld Hstograms, nternatonal Journal of Computer Vson, 6(1): 1 5, [] G. D. Fnlayson, B. Schele and L. J. Crowley, Comprehensve Colour mage ormalzaton, ECCV 98 Ffth European Conference on Computer Vson, Volume : , [4] G. D. Fnlayson, and G. Y. Tan, Perfect Colour Constancy vs Colour ormalzaton for Obect Recognton, SPE, Vol. 826, 1999.

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