Toward Automated Quality Classification via Statistical Modeling of Grain Images for Rice Processing Monitoring

Transcription

1 Iteratioal Joural of Computatioal Itelligece Systems, Vol. 9, No. 1 (2016) Toward Automated Quality Classificatio via Statistical Modelig of Grai Images for Rice Processig Moitorig Jipig Liu* 1, Zhaohui Tag 2, Qig Che 2, Pegfei Xu 1, Wezhog Liu 3, Jiayog Zhu 4 1 College of Mathematics ad Computer Sciece, Hua Normal Uiversity, Chagsha, Hua , Chia 2 School of Iformatio Sciece ad Egieerig, Cetral South Uiversity, Chagsha, Hua , Chia 3 School of Automatio, Huazhog Uiversity of Sciece ad Techology, Wuha, Hubei , Chia 4 School of Electrical ad Electroic Egieerig, East Chia Jiaotog Uiversity Nachag, Jiagxi , Chia *correspodig author, ljp202518@163.com Received 4 Jue 2015 Accepted 7 December 2015 Abstract Computer visio-based rice quality ispectio has recetly attracted icreasig iterest i both academic ad idustrial commuities because it is a low-cost tool for fast, o-cotact, odestructive, accurate ad objective process moitorig. However, curret computer-visio system is far from effective i itelliget perceptio of complex graiy images, comprised of a large umber of local homogeeous particles or fragmetatios without obvious foregroud ad backgroud. We itroduce a well kow statistical modelig theory of size distributio i commiutio processes, sequetial fragmetatio theory, for the visual aalysis of the spatial structure of the complex graiy images. A kid of omidirectioal multi-scale Gaussia derivative filter-based image statistical modelig method is preseted to attai omidirectioal structural features of grai images uder differet observatio scales. A modified LS-SVM classifier is subsequetly established to automatically idetify the processig rice quality. Extesive cofirmative ad comparative tests idicate the effectiveess ad outperformace of the proposed method. Keywords: Process moitorig, Sequetial fragmetatio theory, Weibull distributio, Least squares-support vector machie (LS-SVM) 1. Itroductio With the rapid developmet of the atioal ecoomy ad the improvemet of the livig stadards of the huma beigs, cosumers put forward higher ad higher requiremets o food quality. Rice provides the major source of dietary eergy ad protei, with regard to huma utritio ad caloric itake[1], which is the most widely cosumed staple food for the largest populatio, especially i Chia, who has the largest populatio i the world. Hece, itelliget ad automated rice quality gradig ad classificatio packagig have represeted the core competitiveess of each rice processig compay. For the purpose of providig high quality product, rice, ad reducig the processig cost, most of the rice processig eterprises have cocered the automatic classificatio techology 120

2 rather tha the iefficiet ad subjective maual ispectio of the rice processig-quality. The appearace of the product, rice, icludig color, size, shape, surface texture ad variety of defects, is a effective visual idicator to its iteral quality, e.g., utritio igrediet, taste ad so o, to some extet. Hece, the appearace of the rice product is a very importat sesory attribute for rice quality gradig, which greatly iflueces the prefereces of cosumers ad cosequetly the market value of eterprises. I earlier years, rice quality was ispected maually, thus the accuracy ad efficiecy of the rice processig-quality ispectio results geerally deped o the aked-eye observatio of humas with their subjective experiece. It is worth otig that the maual ispectio is a time-cosumig ad labor-itesive work ad the ispectio results deped largely o huma experiece. Durig the past several years, computer visio-based food quality ispectio system, as a rapid, o-cotact, odestructive, low-cost ad objective tool, has attracted eormous iterest both i academic ad idustrial commuities [2,3]. Brosa[4,5] reviewed the developmets of machie visio-based quality ispectio techiques of food products i a variety of differet applicatios i food idustries i early years. Like the traditioal computer visio moitorig system, the mai steps iclude rice image capturig, processig ad feature extractio ad the ultimate cotet uderstadig, e.g., image-based rice processig quality classificatio or gradig by employig some machie learig ad patter recogitio methods. A more recet elaborate summary of the existig computer visio related exteral quality ispectio of fruits ad vegetables ca be foud i [6], which summarized the priciples, developmets ad applicatios of computer visiobased food quality ispectio ad poited out that it is importat ad ecessary i the postharvest preprocessig stage ad it has become a commo ad scietific tool i idustrial ad agricultural maufacturig automatio. Machie visio-based rice quality ispectio system imitates the visio of the huma eyes by capturig ad aalyzig the rice images of rice products automatically. Thus, digital image processig ad aalysis methods should be adopted defiitely to extract a sufficiet umber of suitable features, which are used for rice quality gradig or process moitorig ad cotrol. Cosequetly, it eables a computer to uderstad the uderlyig cotet of rice images by employig patter classificatio ad recogitio methods[6,7]. I earlier years, people ted to be more cocered about the physical properties of each idividual rice grai, such as surface gloss, physical shape, size ad other characteristics of each rice grai [8]. As addressed i the surveys[5,9], the first step of these methods is rice image segmetatio i geeral, which is the foudatio of gaiig proper descriptios of the surface color, shape ad size of each idividual rice grai. After feature extractio, a kid of classifier such as eural etworks, support vector machies or other supervised patter recogitio methods is established to achieve the automatic gradig or classificatio of rice quality[10]. Although may experimets have verified the effectiveess of these aforemetioed methods with a high gradig accuracy (higher tha 90% recogitio rate), i practical applicatios, may problems exist, for example, the accuracy of the particle image segmetatio ad the efficiecy of image processig (the highest record reported is oly 1200 particles per oe miute [9]). Recet years, researchers expected to bypass the time-cosumig ad usatisfactory image segmetatio process i the rice image processig. Therefore, researchers ted to be more cocered about the spatial variatios (structural distributio) i the gray or color spaces of rice images. Namely, they pay more attetio to the spatial structure features of rice images, usually called as image texture[11]. Texture feature is a statistics-related ad ubiquitous feature i almost every image yet has a great challege to iterpret by the traditioal computer visio techologies. Jackma[7] has poited it out clearly that further aalysis of image texture besides the traditioal image features of the rice image is expected to achieve more sophisticated descriptio to get higher classificatio accuracies. Actually, researchers had focused o the texture features of rice images for rice quality ispectio i the earlier years. The commoly used texture descriptio methods are some simple secod statistics based o the gray level co-occurrece matrix (GLCM), gray level ru legth matrix (GLRM), local biary patter(lbp), histogram descriptio of pixel differece with their varieties ad some other mathematical descriptio methods[7]. 121

3 As research cotiues, multi-resolutio aalysis related methods[12,13], such as Wavelet Trasform, Gabor filters, fractal patter aalysis, have progressively attracted extesive iterest i rice image aalysis ad perceptio for rice quality ispectio. Almost all of these methods attempt to extract statistics i a variety of differet scales or bads to characterize the grai images for cotet uderstadig. Though these methods provide a way of aalyzig rice images uder various observatio scales, the proposed mathematical statistics caot always depict the distict features from the complex texture images for product qualities ispectio or process coditio moitorig, especially from the complex grai images, comprisig a pile of tiy grai particles without distict foregroud ad backgroud. Durig the processig of the polished edible rice, grai particles are radomly stackig o the trasfer belt ad squeezig each other after the rough rice is subject to dehuskig or removal of hulls ad the to the removal of browish outer bra layer kow as whiteig[1], which results i the failure of showig the obvious distictio betwee the foregroud ad backgroud i the collected rice image. Hece, it is geerally difficult to put forward a effective segmetatio method to delieate the specific features of each idividual particle. Furthermore, some local particle-based iformatio is actually isufficiet for rice quality moitorig. The reliable iformatio should be the accumulatig particles radomly piled i the etire observig field, which is described by a certai visual characteristics of macro, i.e., the image statistical distributio characteristics. To obtai the statistical distributio of the pixel itesity or color iformatio i the grai image, curret visio systems either extract some simple statistics, or itroduce some specific empirical statistical distributio models to describe the distributio of the image pixel itesities i a special trasform domai. Commoly used statistics for rice image feature descriptio such as meas or variaces for rice quality perceptio are cofied with the ureliable assumptio that the pixel itesity (i the trasform domai) distributio is Gaussia distributio. Besides the latet Gaussia distributio, the other commoly used statistical distributios models, such as geeralized Laplace distributio[14], Gamma distributio[13], are predefied based o the huma experiece without guidelie for statistical model selectio or evaluatio i theory. The effectiveess of the model selectio greatly affects the accuracy of the fial ispectio results. Sice the proposed statistical parameters or empirical statistical models are short of actual physical sese perceptio mea, they are ofte difficult to effectively characterize the most essetial characteristics of the complex grai images or particle images which are comprised of a large umber of radom local homogeeous particles or fragmetatios. Therefore, it severely restricts the further applicatio of idustrial visio systems i rice-processig-quality moitorig. I order to overcome the problems of uderstadig the complex graiy images comprised of a large umber of local homogeeous particles or fragmetatios, without obvious distictio betwee foregroud ad backgroud, for effective rice quality classificatio, we preset a image statistical modeligbased rice quality ispectio method i this study. We aalyze the Weibull distributio process of the spatial structure of grai images with complex texture feature base o visual perceptio mechaism i advace. I order to obtai the complex spatial structure details of particle images, we itroduce a multi-scale ad omidirectioal Gaussia derivative filterig for image spatial structure feature extractio. The Weibull distributio model parameters are computed as the structural features of rice images for quality ispectio. At last, a LS-SVM classifier is established to automatically idetify the rice quality. 2. Statistical Modelig of Grai Images 2.1. Weibull Distributio Process of Grai Images Rice image is a kid of special grai image with complex texture patter, whose most importat visual structure is the reflectio of the spatial layout of rice particles pilig up i the visual field. As ca be see from Fig.1, a typical rice image is completely covered by a large umber of local homogeeous rice particles which radomly stack ad squeeze each other. These images do ot have distict foregroud ad backgroud. I terms of the machie visio ispectio, if the resolvig power of the visio sesor is high eough, the captured rice image would cotai abudat local structural details of the rice particles. Hece the edges of these local particles would divide the visual scee 122

4 ito umerous idepedet tiy regios, each of which has the same itesity. With the decrease or icrease of the power resolutio of the visual sesor, the adjacet structure will merge or re-subdivide, which leads to form coarser local particle structures or fier local particle structures. Fig.1 Typical grai images Studies have show that the re-subdivisio or mergig processes of the spatial structure details i complex particle images are equivalet to the processes of sequetial fragmetatio[15] well kow i the ore gridig process. Accordig to the theory of sequetial fragmetatio, the size distributio of particles or masses i the particle images subject to a power-law distributio, which could be described by the followig formula: 1 x f x x (1) where x x represets the decompositio process of a larger particle x dividig to the fier grai x, the parameter represets the average size of a local structure fragmets. I this work, it represets the average pixel itesity of each idividual local regio, is a free parameter, satisfy 0. Sice a grai image is composed of a large umber of local homogeeous fragmetatios, the cotrast histogram distributio of the fragmetatios is geerated by the accumulated local particles which obey power-law distributio[15], x c x f x x dx x (2) x represets the histogram distributio fuctio of the pixel cotrast of the local particles. Substitute equatio (1) ito (2) ad solve the equatio as well as set c 1/, we will fid that x actually subjects to a typical Weibull distributio (WD)[15]: 1 1 x x x NT e (3) where NT mdm is a ormalized parameter. 0 Sice the resolvig power of the visual sesor i real applicatios caot be ifiite, the fragmetatio process of the local particles i the rice image will ievitably stop ad the particle details will always ted to be stable. Hece the statistical distributio of the spatial structure of the grai image just correspod to the debris particles whose local cotrast larger tha x. Therefore, the statistical distributio of the spatial structure of the rice image ca be described by the itegral-form WD model, 1 x N x x' dx Ce (4) x 1 where C /2 1/ is a ormalized costat ad x1 t x is the gamma fuctio, x t e dt. Previous studies have emphasized that the itegralform WD model ca effectively portray may classical statistical distributios. As a example, whe 1, WD becomes a expoetial distributio with the mea of.whe 2, the itegral form WD model actually turs out to be a Gaussia distributio. The parameters i the itegral-form WD model have direct correlatios with huma visual perceptio[15], where is a shapedperceptio parameter of the spatial structure reflectig the particle size, shape ad other related visual characteristics, is the scale parameter with a direct relatioship to image illumiatio cotrast. Furthermore, studies have also demostrated that the shape parameter is directly related to the fractal dimesio of the image, the image of the fractal dimesio is D Parameter Estimatio The itegral-form WD model parameters ad ca be estimated by the maximum likelihood estimatio (MLE) method. Give that X x1, x2,, x is the samplig data set, which is subject to the itegral WD model, the correspodig log-likelihood fuctio l LX, idicates how well the model describes the samplig data 1 x i l L, X l e 1 (5) i1 2 1/ The model parameters ca be estimated by settig the partial derivative of l LX, over ad be equal to zero respectively 0 f 123

5 ad 1 xi l LX, 0 i1 1 1 x i l LX, l 2 i1 1 xi x i l 0 i1 d d (6) (7) where x l x x/ x dx dx is the digamma fuctio. The parameter ca be calculated by elimiatig i equatio (7), the we ca get l xi l i1 x i l xi l 1 1 X 1 i1 x 0 Because equatio (8) does ot have a close-form solutio, it ca be solved by the Newto-Raphso method, a iterative procedure of gradiet-based rootfidig method. Give a iitial 0, the iterative procedure is repeated as k X k 1 k k X k where l X x l x X, x x l x x X, i1 i i i i i i i i1 i1 2 xi i1 I equatio (10), i (8) (9) (10) X, l xi l xi l / i1 d ad x x is the trigamma fuctio. dx The iterative procedure is repeated util the estimatio coverges, amely a sufficietly accurate value is reached. After achievig, ca be calculated by the equatio (6). 3. Image Spatial Structure Feature Extractio 3.1. Gaussia Derivative Filterig Supposig a local pixel I 0,0 is the origial observed pixel poit, ay pixel poit xy, i the local spatial structure aroud I 0,0 ca be determied by the Taylor expasio. The approximate expressio of the Taylor formula of Iˆ x, y is as follows: xi 1 0 yi 0 1 x I 2 0 2xyI 1 1 y I 0 2 xy xy 2! x y xy xy ˆ Ix, y I0,0 x I 3 03x yi 2 13xy I 1 2 yi 0 3 x y x y x y x y 3! 1 i i i xi 0 Cxy I i i yi 0 x y x y x y! (11) where C i i!( i)!/! 0 i ad the differetial item I i i xy i Equatio (14) is directly related to the edge iformatio of a image, represetig the most importat spatial structure iformatio. I i i xy ca be obtaied by Gaussia derivative filterig, that is I x, y I x i i, y G i i x, y, xy xy where m,, (12) G x y x y represets a Gaussia derivative filter, the derivative orders of the Gaussia fuctio i the x ad y directios are i ad i respectively, is the Gaussia scale parameter. To simplify the descriptio, we set G, as a -order Gaussia derivative with Gaussia observatio scale of. Fig. 2 shows the WD modelig results of the correspodig spatial structural details of a rice image with first ad secod orders of Gaussia derivative filters ( G1, ad G2, ) respectively. The modelig results explicitly demostrate that the itegral WD model ca effectively characterize the statistical distributio profile of the spatial structure of the complex particle image Omi-directioal Derivative Gaussia Filter Usig G to grai image aalysis ca obtai the, characteristics of the spatial structure of the origi image over x ad y directio. However, the grai structure, as show i Fig.1 ad Fig.2, exhibits obvious orietatio characteristics. Therefore, it is ecessary to itroduce the directio iformatio to the Gaussia derivative filters to obtai the spatial structure feature of the particle image uder ay directio. Deote G as, the result of G, after rotatig a agle of. Accordig to the research of Freema[16], 124

6 the rotated filter formula ca be calculated by the followig (13) (15) The best way to compute is to use the miimum umber of Gaussia derivative bases to represet deoted by the equatio (13). Through selectig Gaussia derivative filter bases of some specific directios ad solvig equatio (18), we ca get the solutio of a omidirectioal Gaussia derivative filter. For example, we ca obtai ad as follows, (16) Fig.3 displays the Gaussia derivative filters of ad i several differet directios betwee [ ~ ] ad respectively. (a) (b) Fig.2 Statistical modelig of rice images with WD. (a)wd modelig results of rice image filterig with first order Gaussia derivative filters,,(b)wd modelig results of rice image filterig with secod order Gaussia derivative filters,, Set the miimum umber of Gaussia derivative filter base as, ad covert to the polar coordiates represeted as, where, the Fourier series decompositio of is (14) From equatio (14), it is ot difficult to fid that the umber of the optimal Gaussia derivative bases is equal to the umber of the o-zero harmoic compoets i the Fourier series decompositio of. Therefore, the solutio is also the solutio of the followig equatio (a) (b) Fig.3 Gaussia derivative filters of several specific orietatios.(a),(b) 3.3. Statistical Characteristics Extractio of Images Spatial Structure To extract the omi-directioal structure features of the rice image, we discretize [0~ ] ito N directios. The WD model parameters of the filterig resposes are extracted as the rice image features. Sice the particle image exhibit obvious directivity, the statistical features of the omi-directioal spatial structure of ay image uder the Gaussia observatio scale ca be described as (17) where represet the maximum ad miimum of model parameter of the omidirectioal [0~ ] uder the Gaussia scale of respectively, ad are the correspodig directio parameters of achievig ad by the WD model, deote the maximum ad miimum 125

7 model parameter, whose correspodig directios are deoted by ad respectively. Fig.4 displays two polar plots of the WD model parameters ad of two differet rice image at the omidirectioal orietatios [0~ ]. As ca be see from Fig.4, the WD model parameters of a grai image exhibit obvious directivity uder a certai observatio scale. The extracted WDmodel parameters are the reflectio of the most importat spatial structural features, which ca be used as the visual hit for rice quality ispectio. I additio, we set a series of differet observatio scales to aalyse the rice image omi-directioally uder diverse observatio scales. Specifically, if the scales of the Gaussia kerel are, the correspodig rice image features ca be expressed as follows. (18) Fig.4 Polar plots of WD model parameter features of two differet rice images 4. Applicatio Case The proposed image statistical modelig based rice quality ispectio method was applied to a riceprocessig eterprise for performace verificatio. To achieve real-time rice-processig-quality ispectio results, a appropriate visual surveillace system is built o a assembly lie of rice processig i a food processig eterprise, i which the visio sesors (camera les of lie-sca digital camera) are vertical to the rice particles o the coveyor belt. Computer visio system achieves olie rice processig-quality idetificatio result based o the image statistical modelig ad aalysis results of the rice images with a itelliget classifier. Whe the visual moitorig system idetifies the uqualified product (rice) o the assembly lie, it will automatically cotrol the actuator (air ozzle) of jet to blow away the low quality rice from productio lie (belt). Fig.5 is a simplified schematic of the computer visio ispectio system for automatic rice processig-quality gradig or classificatio. Fig.5 Schematic diagram of the machie visio based rice processig-quality ispectio system 4.1. Classifier Costructio The task of rice processig-quality gradig is a o-liear classificatio problem, which ca be solved by the supervised patter recogitio or classificatio methods such as eural etworks or support vector machie (SVM). SVM solves the patter recogitio problem through oliear mappig the samples ito a high-dimesioal (eve ifiite dimesioal) feature space, which usually achieves good classificatio accuracy. Furthermore, the Least Square Support Vector Machie (LS-SVM)[17] solves a set of liear equatios istead of a covex quadratic programmig (QP) problem i the classical SVMs, which ca reduce the complexity of the problem. Hece, a LS-SVM classifier is adopted i this work. Accordig to the actual demads of the food eterprise, we oly cosider the two-types classificatio problem of rice quality ispectio. We deote the rice quality correspodig to a sample image with the image feature as Give the correspodig traiig set LS-SVM classifier ca be set as follows: (19), a (20) where is a weight vector, is the deviatio vector, is the possible classificatio error; is a oliear mappig fuctio, which ca map the iput data ito a high dimesioal data space. 126

8 Based o the classificatio model, we costruct the optimizatio problem as, N T 2 mi Jw, b, w w t 2 2 t1 (21) T st.. y t w xtb 1t To solve the optimizatio problem i equatio (21), we ca costruct a Lagrage fuctio uder Karush Kuh Tucker coditios with a Gaussia radial basis fuctio as kerel fuctio Kxi, x k. The, we ca get the rice quality classificatio model f x as N t, t f x a K x x b t1 (22) I the real applicatio, the data distributio is frequetly ubalaced, for example, the umber of samples regardig the high quality is larger tha the umber of the samples of other quality rice. I the commo LS-SVM classificatio model described i the formula (21), each sample is set at a costat pealty parameter r, which is ot a effective solutio for the classificatio of the ubalace distributio data. Hece, i order to treat the samples discrimiatigly i accordace with the differet importace of the samples, differet pealty factors should be provided for each of the samples. The optimizatio problem i formula (21) ca be modified slightly as follows N 1 T r 2 mi Jw, b, w w ct (23) t 2 2 where rct is the pealty factor correspodig to the traiig sample t. To improve the computig performace, the samples of the same types are set the same values as follows, s yt 1 ct (24) s yt 1 The followig steps are similar to the commo LS- SVM model. Hece, by a little modificatio, we ca get a improved LS-SVM (ils-svm) based classificatio model, which ca solve the iaccurate classificatio problem icurred by the ubalaced distributed samples. t Classificatio Results of Rice Quality I order to verify the effectiveess of the proposed method, we collect 5 kids of rice i the assembly lie for rice quality ispectio. The correspodig rice quality resposes are maually labelled by combiig rice appearace ad utriets measured i the laboratory. The rice images are collected by the computer visio moitorig system mouted o the assembly lie. The computer visio ispectio system aalyzes the rice images ad idetifies the rice quality with the ils-svm classifier based o the image feature extractio results. The pixel resolutio of the test ad traiig samples is 2048*256. The processig rate of each chael is about 2 kilogram per secod. (1) Experimetal preparatio We deote the 5 varieties of rice as 1 ~ 5. Durig the rice processig, the rice yield from the processig device is high quality i most cases. It is much coveiet for us to collect the samples of high-quality rice. Hece, the probability distributio of the two classes of rice-quality is ievitably imbalaced. I the verificatio experimet, we have spet about the same time to collect the samples of each varity of rice. After elimiatig the samples without clear rice images, the umbers of the remaiig samples with imbalaced distributios of each variety of rice are displayed i Tab.1. Tab.1 Sample size of the 5 kids of rice quality High: yt Others: yt Sum I the rice-quality classificatio task, the K-folder cross-validatio is a popular ad effective experimetal validatio procedure. Hece, the commoly-used 10- fold cross validatio repeated 10 times is used for classificatio accuracy evaluatio. Namely, each collected data set (both positive ad egative sample) is uiformly divided ito 10 subsets. For the i th time, the i th subset is used as the testig set, ad the other 9 subsets are used as the traiig set. I each repeated experimet, the order of the samples is shuffled to obtai more represetative results. Whe perform the directioal filterig for image aalysis, 180 directioal filter i [0-360 ] are used. The iterval betwee ay two adjacet directios is 2 degrees. 4-scale Gaussia kerels are used for rice image s spatial structure aalysis ad the selected Gaussia scale is [1, 2,2,2 2]. Hece, for each rice image, a kid of vector with 4 8=32 attributes uder a special Gaussia scale is obtaied for feature characterizatio, where 4 meas uder 4 Gaussia observatio scales, 8 represets the 8 parameters determied by the equatio (17). 127

9 (2) Evaluatio measures I terms of biary classificatio problem, 4 metrics are commoly used for the classificatio performace evaluatio[18]. They are: (a) True positive (TP), the umber of positive istaces (i this work, it represets the samples of the other-quality) that are correctly classified; (b) False positive (FP), the umber of egative istaces (i this work, it represets the samples of the high-quality) that are icorrectly classified as positive samples; (c) True egative (TN), the umber of egative istaces that are correctly classified; ad (d) False egative (FN), the umber of positive istaces that are icorrectly classified as egative samples. Based o the metrics, we coduct 3 types of measures to evaluate classifier performace. First of all, the receptive performace evaluatio idex is the total classificatio accuracy (TCA), which ca be computed as TP TN TCA (25) TP TN FN FP Though TCA provides a simple way of evaluatig the classificatio accuracy, it is apparetly ot eough for classificatio accuracy evaluatio because i real rice quality ispectio task, the classificatio errors of differet classes may brig distict ecoomic losses. For example, if we misclassify the sub-quality rice as high-quality rice, it may make the cosumers lose cofidece to the product of the compay ad cosequetly seriously reduce the credit of the food eterprise. However, if we misclassify the high-quality rice as other quality rice, it will reduce the yield ad cause some waste of the food resource. I geeral, the first metioed misclassificatio usually causes greater ecoomic losses. Hece, i this work, we pay more attetio to the correct recogitio of the positive class (the other quality of rice), which has the miority samples. The, i lieu of TCA, other metrics to obtai comprehesive assessmet should be defied. Commoly-used sesitivity (Ses) ad specificity (Spec) are defied as follows: TP Ses TP FN (26) TN Spec (27) TN FP These two measures are aggregate ito a widely used measure, Geometric mea (GM), which is a frequetly used method for comparig positive ad egative classes regardless of their size GM Ses * Spec (28) The measure GM cosiders the classificatio performace both of the miority class ad the majority class. It will achieve high value oly if the classifier gais high classificatio accuracies both o the majority class ad the miority class. If the classifier prefers a certai category, it will affect the accuracy of aother type of sample ad cosequetly lead to small value. The third measure amed F-measure is defied as follows Re call *Pr ecisio F 1 2 Pr ecisio Recall (29) TP TP where Pr ecisio ad Recall. I this TP FP TP FN work we set 1, which meas we give equivalet importace to both precisio ad recall. It is ulike the measure of GM, which pays more isight o the miority class. Hece, we combie the metrics of TCA, GM ad F-measure i this work for the classificatio performace evaluatio. (3) Classificatio results We first evaluate the classificatio accuracies of processig-quality of the 5-varieties of rice by the measure of TCA, which are displayed i Fig.6. I the classificatio experimet, the orders of the Gaussia derivative filters cosidered are first-order, secod-order, third-order ad their combiatios. The metioed with a little modified LS-SVM based classifier is used for rice processig-quality gradig. I the Fig.6, Gi represets the selected Gaussia derivative filter, ad i is the order of the correspodig Gaussia derivative filter. I terms of the result of TCA, whe oly usig a sigle Gaussia derivative filter to idetify the rice quality, G1 could get the best classificatio result, whose average accuracy is %, while the performace of G3 is relatively poor, whose average accuracy of the 5 varieties of rice i the repetitive experimets is oly %. 128

10 combiatio of three differet orders of Gaussia derivative filters will achieve the best total classificatio accuracy, hereby we pay attetio to the rice-image feature by the combied filters ( ). Tab.2 Evaluatio measures of the classificatio results with the proposed WD model parameter features ad ils-svm classifier Rice feature GM F-measure Fig. 6. Total classificatio accuracies (TCA) of the rice processig-quality of 5 kid of rice with the WD statistical features ad LS-SVM classifier If we combie all of the Gaussia derivative filters ( ), the total classificatio accuracy of the 5 varieties of rice would be higher tha 97% i average. Besides the average of TCA (ATCA), the stadard deviatio of TCA (STDTCA) represets the stability of the classificatio performace. By comparig the STDTCA, the combiatio of first to three orders of Gaussia derivative filters ( ) is a fairly stable rice image aalysis method. The STDTCA regardig filter is as low as 1.57 (%) i average for all 5-varieties of rice i the repetitive experimets, while the STDTCAs regardig,,,, are 2.4(%), 3.2(%), 2.5(%), 4.2(%),3.8(%) i average for all the 5-varieties of rice. Besides the evaluatio measure of TCA, more classificatio accuracy evaluatio results ca be fid i Tab.2, where oly the average value of the metrics of the 10 repeated experimets are provided. As ca be see from Tab.2, the evaluatio metrics GM ad F- measure by various combiatios of the Gaussia derivative filters will achieve differet performace. However, the same as TCA, the WD model parameter features with the full combiatio of the Gaussia derivative filters ( ) ca achieve the best performace o o matter what kids of rice based o the measures of GM ad F-measure. Combiig the classificatio results of Fig.6 with Tab.2, it idicates that the proposed statistical modelig based image feature with the ils-svm classifier is a effective productio-quality ispectio method, especially with the combiatio filters of, which is also a stable rice-quality classificatio method with high classificatio accuracy ad ca effectively esure the actual demad of automatic recogitio of the rice quality i rice processig eterprise. Sice the (4) Performace compariso I order to compare the performace of the proposed method with some other related methods, we selected some well kow image-based rice quality classificatio. The related feature extractio methods are the gray level co-occurrece matrix (GLCM)[19], the gray level ru legth matrix (GLRM)[19] ad Wavelet trasform aalysis method[20]. GLCM / GLRM extractio details are as follows: (a) The image itesity is quatized to 8, 32 ad 64 brightess levels; (b) At each quatizatio scale, calculate the GLCM / GLRM matrix; (c) Based o each GLCM / GLRM matrix, we extract i total of 14 statistics[19], e.g., eergy, iertia 129

11 momet, partial correlatio, etropy, fieess, to costitute the spatial structural feature vector of the rice images. The details of WTA feature extractio are as follows: (a) Image colour space are trasformed ito HIS ad CIE L*a*b* color spaces; (b) I each idepedet colour space, we coduct multi-scale image decompositio usig Db4 wavelet for rice image aalysis, util the coarsest scale of the image size is ot less tha 8*8; (c) I each decompositio scale, we calculate eergy, colour covariace, etc. I total of 15 parameters[21] computed from the detail Wavelet decompositio coefficiets. I the comparative experimets, the classifier selectio is aother effect factor to the classificatio performace. Besides the proposed ils-svm based classifier, the commoly used learig vector quatizatio eural etwork (LVQ-NN)[22] based classifier is used for rice-quality classificatio experimet. I each comparative experimet, the traiig ad test samples used for five differet kids of rice are idetical. The umber of hidde layer of LVQ-NN is determied by the best classificatio performace through cross validatio. Procedure of 10-fold crossvalidatio repeated 10 times is used i the comparative experimet. By the repetitive experimets with differet traiig samples, we fid that 24 hidde layer odes could obtai the best recogitio results. The classificatio results achieved by combiatio of differet image features with differet classifiers are displayed i Tab.3, where G i still meas the i th order Gaussia derivative filter. As ca be see from Fig.6, Tab.2 ad Tab.3, the average TCA of the five rice-varieties by the proposed statistical feature ( G1 G2 G3) ca reach over 97% ad 90% based o the ils-svm classifer ad LVQ-NN classifier respectively. Whereas, the TCA based o the commoly-used features of GLCM, GLRM, WTA ad GLCM+GLRM with ils-svm oly achieves 83.5%, 82.8%, 85.1%, ad 86.2%, however if we chage the ils-svm classifier to the LVQ-NN classifier, there is o improvemet of the TCA based o these features with LVQ-NN classifier, which ca oly achieve 82.8%, 82.7%, 83.4% ad 86.2% accuracy rate. Hece, it ca achieve much better classificatio performace (higher TCA) by the proposed statistical feature ( G1 G2 G3), whether we use ils-svm classifier or the LVQ-NN classifier. I terms of the classificatio accuracies of the differet classes of samples, the best classificatio results ca be achieved from the proposed statistical feature ( G1 G2 G3) with ils-svm classifier. As ca be see from Tab.2 ad Tab.3, the average GM ad F- measure of the five rice-varieties is ad based o the proposed statistical feature ( G1 G2 G3) with ils-svm. Ad the average GM ad F-measure based o the combiatio of the proposed statistical feature ( G1 G2 G3) with LVQ-NN are much lower the proposed statistical feature with ils-svm classifier, they ca oly reach ad respectively, which meas the combiatio of the proposed statistical feature with ils-svm classifier ca achieve better classifier accuracies both o the majority samples ad the miority samples. The combiatios of the other commoly-used features, e.g, GLCM, GLRM, WTA, with the classifiers ils-svm ad LVQ-NN, are geerally iferior to the proposed statistical feature ( G1 G2 G3) with ils-svm classifier. The average GM of the five rice-varieties based o the ils-svm classifier with the GLCM, GLRM, WTA ad GLCM+GLRM feature ca oly achieve 0.847, 0.834, ad 0.876, whereas the average F-measures based o these features with LVQ- NN classifier are 0.826, 0.836, ad The average F-measure of the five rice-varieties based o the ils-svm classifier with the GLCM, GLRM, WTA ad GLCM+GLRM features ca oly achieve 0.848, 0.845, ad 0.878, ad the the average F-measure based o the LVQ-NN classifier with these commoly-used features are 0.839, 0.850, ad respectively. Apparetly, whether the ils-svm classifier or LVQ-NN classifier is used, the proposed statistical feature G 1 G 2 G 3 is superior to the commoly used feature extractio methods for ricequality classificatio. Combied the experimetal results i Fig.6, Tab.2 ad 3, it ca be see apparetly that the proposed method, WD model features with the improved LS- SVM classifier, achieves much higher classificatio accuracy with stable results both o the positive samples ad the egative samples, which ca be effectively applied to the itelliget rice quality ispectio i the assembly productio lie. 130

12 Tab.3 Comparative results of rice quality classificatio with the combiatio of differet image features ad differet classifiers Rice feature classifier TCA GM F-measure Rice feature classifier TCA GM F-measure G1G2 G3 LVQ-NN G1G2 G3 LVQ-NN GLCM ils-svm GLCM ils-svm GLCM LVQ-NN GLCM LVQ-NN GLRM ils-svm GLRM ils-svm GLRM LVQ-NN GLRM LVQ-NN WTA ils-svm WTA ils-svm WTA LVQ-NN WTA LVQ-NN GLCM+GLRM ils-svm GLCM+GLRM ils-svm GLCM+GLRM LVQ-NN GLCM+GLRM LVQ-NN G1G2 G3 LVQ-NN G1G2 G3 LVQ-NN GLCM ils-svm GLCM ils-svm GLCM LVQ-NN GLCM LVQ-NN GLRM ils-svm GLRM ils-svm GLRM LVQ-NN GLRM LVQ-NN WTA ils-svm WTA ils-svm WTA LVQ-NN WTA LVQ-NN GLCM+GLRM ils-svm GLCM+GLRM ils-svm GLCM+GLRM LVQ-NN GLCM+GLRM LVQ-NN G1G2 G3 LVQ-NN GLCM ils-svm GLCM LVQ-NN GLRM ils-svm GLRM LVQ-NN WTA ils-svm WTA LVQ-NN GLCM+GLRM ils-svm GLCM+GLRM LVQ-NN Coclusios This work presets a image statistical modeligbased method for automated rice quality ispectio. We have aalyzed the WD distributio process theoretically of the spatial structure of the complex grai images, which comprised of a large umber of local homogeeous particles or fragmetatios without obvious foregroud ad backgroud. A kid of multiscale ad Omidirectioal Gaussia derivative filterig method is proposed to obtai the spatial structures of the grai images. Ad the WD model parameters of the filterig resposes are achieved for grai image feature characterizatio. A improved LS-SVM classifier was employed to rice processig-quality classificatio with the extracted image features based o the statistical model. The experimets demostrate that the proposed method ca effectively describe the characteristic of the complex graiy images ad possesses a high accuracy for rice quality classificatio, which effectively esured the itelliget classificatio of high-quality rice ad automatic packig i the assembly lie rice productio lie. 131

13 Ackowledgmets This work is supported by the Natioal Natural Sciece Foudatio of Chia uder grat Nos , , , ad , the Youg Teacher Foudatio of Hua Normal Uiversity uder Grat Number 11405, the Sciece ad Techology Plaig Project of Hua Provice of Chia uder Grat Number 2013FJ4051 ad Scietific Research Foudatio of Educatioal Commissio of Hua Provice of Chia uder Grat Number 13B065. Refereces 1. Yadav, B.K.; Jidal, V.K. Moitorig millig quality of rice by image aalysis. Computers ad Electroics i Agriculture 2001, 33, Wu, D.; Su, D.-W. Colour measuremets by computer visio for food quality cotrol a review. Treds i Food Sciece & Techology 2013, 29, Jai, N.K.; Khaa, S.; Jai, K.R. I Developmet of a classificatio system for quality evaluatio of oryza sativa l.(rice) usig computer visio, Commuicatio Systems ad Network Techologies (CSNT), 2014 Fourth Iteratioal Coferece o, 2014; IEEE: pp Brosa, T.; Su, D.-W. Ispectio ad gradig of agricultural ad food products by computer visio systems a review. Computers ad Electroics i Agriculture 2002, 36, Brosa, T.; Su, D.-W. Improvig quality ispectio of food products by computer visio a review. Joural of Food Egieerig 2004, 61, Zhag, B.; Huag, W.; Li, J.; Zhao, C.; Fa, S.; Wu, J.; Liu, C. Priciples, developmets ad applicatios of computer visio for exteral quality ispectio of fruits ad vegetables: A review. Food Research Iteratioal 2014, 62, Jackma, P.; Su, D.-W. Recet advaces i image processig usig image texture features for food quality assessmet. Treds i Food Sciece & Techology 2013, 29, Yadav, B.; Jidal, V. Moitorig millig quality of rice by image aalysis. Computers ad Electroics i Agriculture 2001, 33, Brosa, T.; Su, D.-W. Ispectio ad gradig of agricultural ad food products by computer visio systems a review. Computers ad Electroics i Agriculture 2002, 36, iatig rapeseed varieties usig computer visio ad machie learig. Expert Systems with Applicatios 2015, 42, Rahma, A.; Murshed, M. Feature weightig ad retrieval methods for dyamic texture motio features. Iteratioal Joural of Computatioal Itelligece Systems 2012, 2, Wag, Z.; Chag, J.; Zhag, S.; Luo, S.; Jia, C.; Su, B.; Jiag, S.; Liu, Y.; Liu, X.; Lv, G. Applicatio of wavelet trasform modulus maxima i rama distributed temperature sesors. Photoic Sesors 2014, 4, Liu, J.; Gui, W.; Tag, Z.; Hu, H.; Zhu, J. Machie visio based productio coditio classificatio ad recogitio for mieral flotatio process moitorig. Iteratioal Joural of Computatioal Itelligece Systems 2013, 6, Yu, L.; He, Z.; Cao, Q. Gabor texture represetatio method for face recogitio usig the gamma ad geeralized gaussia models. Image ad Visio Computig 2010, 28, Geusebroek, J.-M.; Smeulders, A.W. A six-stimulus theory for stochastic texture. Iteratioal Joural of Computer Visio 2005, 62, Freema, W.T.; Adelso, E.H. The desig ad use of steerable filters. IEEE trasactios o patter aalysis ad machie itelligece 1991, 13, Ghaedi, M.; Ghaedi, A.; Hossaipour, M.; Asari, A.; Habibi, M.; Asghari, A. Least square-support vector (lssvm) method for modelig of methylee blue dye adsorptio usig copper oxide loaded o activated carbo: Kietic ad isotherm study. Joural of Idustrial ad Egieerig Chemistry 2014, 20, Peg, L.; Zhag, H.; Yag, B.; Che, Y. A ew approach for imbalaced data classificatio based o data gravitatio. Iformatio Scieces 2014, 288, Majumdar, S.; Jayas, D. Classificatio of cereal grais usig machie visio: Iii. Texture models. Trasactios of the ASAE 2000, 43, Cocchi, M.; Corbellii, M.; Foca, G.; Lucisao, M.; Pagai, M.A.; Tassi, L.; Ulrici, A. Classificatio of bread wheat flours i differet quality categories by a waveletbased feature selectio/classificatio algorithm o ir spectra. Aalytica Chimica Acta 2005, 544, Choudhary, R.; Paliwal, J.; Jayas, D. Classificatio of cereal grais usig wavelet, morphological, colour, ad textural features of o-touchig kerel images. Biosystems egieerig 2008, 99, Kohoe, T. I Improved versios of learig vector quatizatio, Neural Networks, 1990., 1990 IJCNN Iteratioal Joit Coferece o, 1990; IEEE: pp