Stone Images Retrieval Based on Color Histogram
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1 Stoe Images Retrieval Based o Color Histogram Qiag Zhao, Jie Yag, Jigyi Yag, Hogxig Liu School of Iformatio Egieerig, Wuha Uiversity of Techology Wuha, Chia Abstract Stoe images color features are chose for its retrieval ad idexig i this paper. Cross color histogram, aular color histogram, ad agular color histogram are respectively combied with HSV color space to accord with huma s visual uiformity. I HSV color space, H, S, V three compoets carry o ueual iterval uatizatio to improve retrieval precisio ad efficiecy, ad the differet weight values are give ad adjusted to differet color chael. Stoe images are sorted accordig to their similarity i retrieval results. Experimets showed that cross color histogram i HSV color space has excellet performace o stoe images retrieval. I. INTRODUCTION Alog with the developmet of multimedia techology ad the arrival of iformatio age, image iformatio resources have icreased rapidly. Traditioal image database retrieval based o keywords or descriptive text is o loger eough. Therefore, cotet-based image retrieval is proposed to extract images visual feature by computers, icludig color, texture, shape, positio of the objects ad their iterrelatioship, etc. Amog them color is perhaps the most direct ad distiguishig visual feature; it is also ivariat to traslatio ad rotatio. So far, color histogram is the most widely used color descriptor i cotet based retrieval research. Several methods based o color histogram have bee proposed i image retrieval. They are maily divided ito two types: global feature idex ad local feature idex. The techology of global feature idex [4] was first proposed by Swai ad Ballad, the mai idea of which is to aalyze each color s statistical freuecy i a image. However, with this way, the special iformatio of colors is ot preserved. Pass ad Zabih defie the cocept of color coheret vector (CCV) ad use it to segmet a color histogram ito two parts. A pixel is called coheret if its coected compoet is over a certai value while the opposite is called o-coheret. A CCV of a image is the statistical ratio of each color s coheret ad o-coheret pixels; this way, the special iformatio is preserved to a certai degree. Typical local feature idex icludes color histogram based o represetative colors, which splits a image ito several parts ad selects a represetative color for each part. Stricker ad Damai cosider that the ceter of a image as the most sigificat part. They partitio a image ito 5 fuzzy regios, ad the extract the first three momets of the color distributio for each regio. With the method of local feature idex, the special distributio of a image is cosidered coupled with color. Take together, there are already several techiues researched for the retrieval of color images. For stoe images, color is also cosidered as a typical feature for retrieval ad idexig. Colors distribute evely i a stoe image, furthermore, the image has o defiite objects ad ca t be distiguished as backgroud ad foregroud. I this paper, the color space is firstly trasformed from RGB ito HSV color space, uatized with proper uatizatio parameters, ad the three algorithms are used for the stoes image retrieval: cross color histogram, aular color histogram, ad agular color histogram. Accordig to the results of experimets, aalyses ad comparisos are made to their retrieval effect. II. COLOR-SPACE CONVERSION HSV color space is color space model desiged for huma visio cosideratio. Hue, saturatio ad value are used to describe colors i this color space. They ca preferably reflect huma perceptio ad idetificatio of color images. I HSV color space, hue is distiguished by differet color, such as red, gree, which is measured by agle raged i 0~360 saturatio refers to the cocetratio of color which is measured by percetage; value refers to a image s lumiace which is measured by percetage as well [3]. This is illustrated i Figure. V Gree (20 ) Blue (240 ) 0 Red (0 ) Fig..HSV color space model. For a true color image, color histogram after color-space coversio will still have high dimesio. Therefore, i order to reduce calculatio time ad ehace retrieval efficiecy, proper uatizatio is ecessary. I this paper, the three compoets H, S, V are uatized i ueual iterval respectively accordig to huma s visual resolvig power ad subjective perceptio. H S /09/$ IEEE Authorized licesed use limited to: Rochester Istitute of Techology. Dowloaded o December 4, 2009 at 0:46 from IEEE Xplore. Restrictios apply.
2 0 h [ 36, 360]»ò[ 0, 20] h [ 2, 40] 2 h [ 4, 75] 3 h [ 76, 55] H = () 4 h [ 56, 90] 5 h [ 9, 270] 6 h [ 27, 295] 7 h [ 296, 35] 0 s [ 0, 0. 2] S = s ( 0. 2, 0. 7] (2) 2 s ( 0. 7, ] 0 v [ 0, 0. 2] V = v ( 0. 2, 0. 7] (3) 2 v ( 0. 7, ] Now the color space is partitioed ito 833=72 color eigevalues. Followig the above uatizatio levels, the three compoets of color ca be weighted respectively ad trasformed ito a oe-dimesio vector L = HQ Q + SQ + V (4) s v v Here Q s ad Q v refer to the uatizatio levels of S ad V. Take Qs=3, Qv =3, the formula ca be expressed as L = 9H + 3 S + V (5) This way, the weight value ca fully capture the images color iformatio. Moreover, it reduces the ifluece of a image s lumiace. For the oe-dimesio vector L, it rages from 0 to 7. Color histogram is created by statistics of the 72 color eigevalues. III. THREE ALGORITHMS BASED ON HISTOGRAM Cross color histogram [4] is first proposed by Swai ad Ballad. It is a oe-dimesio discrete fuctio expressed as k H( k) = k = 0,,... l (6) N I the formula, k represets the umber of pixels while the eigevalue is k, while N is the total umber of pixels i a image. l refers to the umber of eigevalues after uatizatio, ad also it is the elemet umber of vector L. Let H Q ad H D be the color histograms of ueried image Q ad target image D, the similarity matchig betwee the two images is: PQD (, ) = l k = 0 [ H k H k Q D ] mi ( ), ( ) l k = 0 H ( k) Formula 7 describes the proportio of the histogram of the cross part i that of the ueried image. It is illustrated i Figure 2. Q (7) H Fig. 2.Cross color histogram. The calculatio procedure is simple whe usig the method of cross color histogram. It is also isesitive to scale, traslatio ad rotatio. However, global histogram ca t capture the special distributio of colors. Retrieval effect is limited whe two images have similar colors but totally differet distributio. So, color histogram icludig the special iformatio is also adapted ito the stoe image retrieval. Accordig to Aibig Rao s aular ad agular histograms [5], the distributio of colors i a image ca be well described. Let p ij CR be a image of size CR where p ij is the color of pixel(i,j). Set {(, ) ; } U = x y x R y C, BB,... B are the, 2 M eigevalues after uatizatio with M color bis. Let {(, ) (, ), } S = x y x y U p B for M, the xy M U = S = subset of bi B, is the set of pixels whose color is i the th bi. Now cosider the histogram subset S as a geometric distributio o the 2-D plae for each color bi B. let C = ( x, y ) be the cetroid of S, where x ad y are defied as follows x = x y = y S ( x, y) s S ( x, y) s (8) The the radius of S is defied as r max ( = x x ) 2 + ( y y ) 2 (9) x,y S Give a umber N, divided the radius ito N parts evely, ad the draw N cocetric circles with ueried image Q 0 7 l kr N target image D as the radius for each k N to form N aular regios. Each regio, from ier to outside, is defied as R, R,..., R.Vector 2 N ( R, R,..., R ) is called the aular distributio 2 N desity. This is illustrated i Figure 3. Authorized licesed use limited to: Rochester Istitute of Techology. Dowloaded o December 4, 2009 at 0:46 from IEEE Xplore. Restrictios apply.
3 Fig. 3.Aular distributio desity. Set A = R for i = 2,,..., M ad j = 2,,..., N. ij ij The a MN matrix A = ( A ) is called aular histogram ij M N of the image which refers to the umber of a certai color i a certai rig. Sice the cetroid ad the aular partitio of each histogram subset are traslatio ad rotatio ivariat, the histogram is tolerat to a small movemet before the image is processed, the Euclidea distace is adopted to calculate the similarity distace which is defied as M N 2 ( ij ij ) d = A B i= j= (0) Notice that whe N=, the matrix represets the global histogram. Similar to the aular partitio, agular partitio itroduces aother type of special color histogram. I order to keep the advatage of traslatio ad rotatio ivariat, a startig directio should be chose at first. Let C = ( x, y ) is the cetroid of S. For each poit ( x, y) S, the startig directio ca be calculated via the followig formula y y ( x, y) = arcta ± () x x Here +, - are to selected depedig o which uadrat the poit is i. The the average directio of the subset S is deoted as ( S ) = (, ) x y (2) S ( x, y) S This average directio is the startig directio of agular partitio. Give a umber N, for each histogram subset S, startig from the average directio ( S ), divided the uit circle cetered at C evely ito N fa-like domais. From the startig directio, the agular domais are amed R, R 2,...R N i tur. Vector ( R, R 2,... R N ) is called agular distributio desity of the subset S. this is illustrated i Figure 4. Fig. 4.Agular distributio desity. It is obvious that agular partitio is also tolerat to the traslatio ad rotatio of images. IV. EXPERIMENTS AND COMPARISONS Special stoe images database is used i this paper. Here graite is chose to extract its color feature. The total umber of graite is 26, with 4 couples of similar colors: red, yellow, grey ad cya. The arragemet is made by experts before experimets. These stoe images are retrieved ad idexed through the above algorithms. The result is ordered accordig to the similarity from miimum to maximum to compare the retrieval effect of each color histogram. At preset, the evaluatio of retrieval effect maily focuses o the accuracy of the result. The stadard of accuracy mostly depeds o precisio ad recall. Precisio refers to the ratio of similar images umber ad the total images umber i the retrieval result. Recall refers to the ratio of similar images umber i the result ad i the database. Let r the images umber i the retrieval result, s is similar images umber of it. The total images umber i the database is. Precisio ad recalls [6] are defied as precisio r = s s recall = (3) The retrieval effect will be better whe the precisio ad recall are higher. Geerally, precisio ad recall are usually tradeoffs. If high precisio is eeded, recall will usually be reduced, ad vice versa. Geerally a good retrieval system is balace the performace betwee precisio ad recall. Partial retrieval results of red graite are i showed i Figure 5, 6, 7. I Figure 5, 6, 7, pictures are amed by this way: idex-score. Idex is the serial umber of images after sortig ad score meas the similarity scorig for each image by experts. Figure 8 is the statistical result of all the images. Authorized licesed use limited to: Rochester Istitute of Techology. Dowloaded o December 4, 2009 at 0:46 from IEEE Xplore. Restrictios apply.
4 Fig. 5.The first four images after sortig by cross color histogram. Fig. 6.The first four images after sortig by aular color histogram. colors Fig. 7.The first four images after sortig by agular color histogram. Cross color histogram Aular color histogrm Agular color histogram s prec rec s prec rec s prec rec red yellow gray cya Fig. 8.Comparisos of precisio ad recall with three differet algorithms ( r=8). Figure 8 lists the results of all the 26 stoe images. Three of the algorithms have good effect for stoe images retrieval accordig to the list. Comparatively, the performace of cross color histogram outperforms the other two. With certai recall Authorized licesed use limited to: Rochester Istitute of Techology. Dowloaded o December 4, 2009 at 0:46 from IEEE Xplore. Restrictios apply.
5 rate, precisio has better effect. Moreover, the sortig of similarity by experimets ad experts matches well. V. CONCLUSIONS Due to the special color feature of stoe images, which have evely distributed colors ad o defiite objects, three color histograms respectively combied with HSV color space is adopted for retrieval ad idexig i this paper. With the advatage of visual uiformity i HSV color space, global ad local feature are both extracted from the stoe images. Experimets show that, with a lower dimesio ad less computig load, the cross color histogram i HSV color space has better effect for stoe images. I later work, i order to improve the retrieval precisio more, the texture feature of stoe images will be cosidered coupled with its color feature. REFERENCES [] M.J.Swai ad D.H.Ballard, Color Idexig, Iteratioal Joural of Computer Visio, 99, Vol. 7, No., pp.-32. [2] G. Pass ad R. Zabih, Histogram Refiemet for Cotet-Based Image Retrieval, IEEE Workshop o Applicatios of Computer Visio, pp , December 996. [3] X. Y. Li, Y. X. Zhuag ad Y.H. Pa, The Techiue ad Systems of Cotet-Based Image Retrieval, Joural of computer research ad developmet, 200, Vol. 38, No. 3, pp [4] C. Y. Wu, X.Y. Tai ad J.Y. Zhao, Image Retrieval Based o Color Feature, Computer applicatios, 2004, Vol. 24, No. 6, pp [5] A.B. Rao, R. K. Srihari ad Z. F. Zhag, Spatial Color Histograms for Cotet-Based Image Retrieval, Proceedig of IEEE Iteratioal Coferece o tools with artificial Itelligece, November, 999, pp [6] X.J. Zhag, Research o color feature based image retrieval. Master degree thesis, Liaoig techical uiversity, Authorized licesed use limited to: Rochester Istitute of Techology. Dowloaded o December 4, 2009 at 0:46 from IEEE Xplore. Restrictios apply.
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