PROBABILISTIC BASED ROCK TEXTURE CLASSIFICATION

PROBABILISTIC BASED ROCK TEXTURE CLASSIFICATION R.Vnoth 1, R.Srnvasan 2, D.Vmala 3, M.M.Arun Prasath 4, D.Vnoth 5 1 AP, Dept of ECE, Muthayammal College of Engg, Raspuram, Taml Nadu, Inda, 2 AP, Dept of EEE, Muthayammal College of Engg, Raspuram, Taml Nadu, Inda, 3 AP, Dept of EEE, PGP College of Engg & Tech, Namaal, Tamlnadu, Inda, 4 AP, Dept of ECE, Annapoorana Engneerng College, Salem, Taml Nadu, Inda, 5 AP, Dept of EEE, Muthayammal College of Engg, Raspuram, Taml Nadu, Inda ABSTRACT The classfcaton of natural mages s an essental tas n computer vson and pattern recognton applcatons. Roc mages are the typcal example of natural mages, and ther analyss s of maor mportance n roc ndustres and bedroc nvestgatons. Rocs are manly classfed nto three types. They are Igneous, Metamorphc and Sedmentary. They are further classfed nto Andeste, Basalt, Amphbolte, Grante, Brecca, Coal and etc In ths proect classfcaton s done n three subdvsons. Frst the gven roc mage s classfed nto maor class. Next t s classfed nto subclass. Fnally the group of coal mages s segmented and classfed usng Tamura features, Probablstc Latent Semantc Analyss PLSA) and Sum of Square Dfference classfer. Roc mage classfcaton s based on specfc vsual descrptors extracted from the mages. Usng these descrptors mages are dvded nto classes accordng to ther vsual smlarty. Ths proect deals wth the roc mage classfcaton usng two approaches. Frstly the textural features of the roc mages are calculated by applyng Tamura features extracton method. The Tamura features are Coarseness, Contrast, Drectonalty, Lne leness, Roughness and Regularty, Smoothness and Angular second moments. In next step calculated Tamura features are appled to Probablstc Latent Semantc Analyss PLSA) to generate a topc model. Ths topc model s appled to SSD classfer to classfy the roc mage nto one of the maor class. Smlarly the roc textures are classfed nto subclass, and the group of coal mages s segmented and classfed. Ths method s compared wth Gray Level Co-occurrence Matrx GLCM) method and Color Co-occurrence Matrx method. Ths method gves a better accuracy when compared to those methods. Ths technque can readly be appled to automatcally classfy the rocs n such felds of roc ndustres and bedroc nvestgatons. I. INTRODUCTION 1.1. Rocs Rocs are among the most basc thngs on Earth. They can be found ust about everywhere. They do not seem very exctng at frst glance. But there s a lot more n rocs than frst meets the eye. There are several types of rocs, whch are very useful n many felds le marble, grante, coal, quartzte and etc. The classfcaton of rocs s very essental n roc ndustres and bedroc nvestgatons, whch has been done manually. 1.1.1. Igneous Igneous rocs form from molten rocs, whch are thc, flud masses of very hot elements and compounds. There are many dfferent types of gneous rocs. However, they were once melted and have snce cooled down. The two maor factors that nfluence the creaton of gneous rocs are the orgnal roc that was melted and the coolng hstory of the molten rocs 1.1.2. Metamorphc 2439 Vol. 6, Issue 6, pp. 2439-2447

Metamorphc rocs are formed manly n the lthosphere or crust and upper mantle, wherever there s hgh pressure and hgh temperature. Metamorphc rocs record how temperature and pressure affected an area when t was formng. The rocs provde clues to ther transformaton nto metamorphc rocs 1.1.3. Sedmentary Sedmentary rocs form only on the surface of the Earth. Sedmentary rocs form n two man ways, from clastc materal peces of other rocs or fragments of seletons) that are cemented together, and by chemcal mechansms ncludng precptaton and evaporaton. There are many envronments assocated wth sedmentary roc formaton ncludng oceans, laes, deserts, rvers, beaches, and glacers. They may form at all types of plate boundares, but the thcest sedmentary roc accumulatons occur at convergent plate boundares. Fossls are assocated wth sedmentary rocs. 1.2 Scope Of The Thess Fgure 1.1.The Roc Cycle Rocs are used n varous ndustres and varous places. For example marbles are used n fancy floorng and countertops, sculptures and carvngs, decor on front porch and etc Slate s wdely used as roofng materal. Shale s used as fller for pant. Obsdan can be used for ewelry and nfe and sandstone as Glassware. Coals are used for generaton of electrcty, whch s a maor requrement nowadays. So the classfcaton of rocs and coals s very essental n roc and coal ndustres. The roc mages can effcently be classfed usng the method proposed n ths thess. Ths can be appled n roc ndustres to classfy the rocs automatcally. And also the automatc classfcaton of rocs s very essental n bedroc nvestgatons, coal mnes and plannng for ol reservors. II. 2.1. Texture TEXTURE ANALYSIS In many machne vson and mage processng algorthms, smplfyng assumptons are made about the unformty of ntenstes n local mage regons. However, mages of real obects often do not exhbt regons of unform ntenstes. For example, the mage of a wooden surface s not unform but contans varatons of ntenstes whch form certan repeated patterns called vsual texture. The patterns can be the result of physcal surface propertes such as roughness or orented strands whch often have a tactle qualty, or they could be the result of reflectance dfferences such as the color on a surface. To enhance the mage we are usng some of the technques le Morphologcal flterng, spatal doman flterng, wavelet based flterng etc.. 2.2 Texture Classfcaton Classfcaton refers to as assgnng a physcal obect or ncdent nto one of a set of predefned categores to measure the parameters. In texture classfcaton the goal s to assgn an unnown sample mage to one of a set of nown texture classes. Texture classfcaton s one of the four problem domans n the feld of texture analyss. The other three are texture segmentaton parttonng of an mage nto regons whch have homogeneous propertes wth respect to texture; supervsed texture segmentaton wth a pror nowledge of textures to be separated smplfes to 2440 Vol. 6, Issue 6, pp. 2439-2447

texture classfcaton), texture synthess the goal s to buld a model of mage texture, whch can then be used for generatng the texture) and shape from texture a 2D mage s consdered to be a proecton of a 3D scene and apparent texture dstortons n the 2D mage are used to estmate surface orentatons n the 3D scene). 2.3. Texture Features In the case of mage classfcaton, features extracted from mages are employed. The extracted features used n mage classfcaton relate to the colors, textures and shapes occurrng n the mages. There are many dfferent feature extracton methods that were ntroduced and used for texture classfcaton problems. Most of these methods that were popularly used n recent years were statstcal and sgnal processng methods. 2.3.1. GLCM Grey Level Co-occurrence Matrces GLCM) s an old feature extracton for texture classfcaton that was proposed by Haralc et al. It has been wdely used on many texture classfcaton applcatons and remaned to be an mportant feature extracton method n the doman of texture classfcaton. It s a statstcal method that computes the relatonshp between pxel pars n the mage. In the conventonal method, textural features wll be calculated from the generated GLCMs, e.g. contrast, correlaton, energy, entropy and homogenety 2.3.2. Local Bnary Patterns LBP) The orgnal LBP s proposed by Oala and Petanen bac n 1999. The orgnal LBP calculates a value that reflects the relatonshp wthn a 3 3 neghborhood through a threshold neghborhood that s multpled wth the respectve bnomal weghts. Snce the LBP s used to calculate local features, t s often used for texture segmentaton problems. It has yet to be a very popular method n texture classfcaton problem. 2.3.3. Gabor Flters Gabor flters s a popular sgnal processng method, whch s also nown as the Gabor wavelets. The Gabor flters are defned by a few parameters, ncludng the radal center frequency, orentaton and standard devaton The Gabor flters can be used by defnng a set of radal center frequences and orentatons whch may vary but usually cover 180 n terms of drecton to cover all possble orentatons. Due to the large feature sze produced by sgnal processng methods, the Gabor flters requres to be downszed to prevent the curse of dmensonalty. Prncpal Component Analyss PCA) s one of the popular methods to downsze the feature space. Gabor flter III. PROBLEM STATEMENT In the roc ndustry, the vsual nspecton of products s essental because the color and texture propertes of roc often vary greatly, even wthn the same roc type. Therefore, when roc plates are manufactured, t s mportant that the plates used, such as n floorng, share common vsual propertes. In addton, vsual nspecton s necessary n the qualty control of roc products. Tradtonally, roc products have been manually classfed nto dfferent categores on the bass of ther vsual smlarty. However, n recent years the roc and stone ndustry has adopted computer vson and pattern 2441 Vol. 6, Issue 6, pp. 2439-2447

recognton tools for use n roc mage nspecton and classfcaton. Compared to manual nspecton and classfcaton, the use of automated mage analyss provdes several benefts. Manual nspecton carred out by people s, as mght be affected by human factors. These factors nclude personal preferences, fatgue, and the concentraton levels of the ndvdual performng the nspecton tas. Therefore, nspecton s a subectve tas, dependent on the personal nclnatons of the ndvdual nspector, wth ndvduals often arrvng at dfferent udgments. By contrast, automated nspecton by computer wth a camera system performs both nspecton and classfcaton tass dependably and consstently. Another drawbac of manual nspecton s the amount of manual labor expended on each tas. Addtonally, n the feld of roc scence, the development of dgtal magng has made t possble to store and manage mages of the roc materal n dgtal form. One typcal applcaton area of roc magng s bedroc nvestgaton whch s utlzed n many areas from coal mnng to geologcal research. In coal mnng the varous types of coal would be accumulated or gathered n same place. 3.1. Gray level co-occurrence matrx method In ths method the gven roc mage s classfed nto maor class and sub class. These classfcatons are as follows. 3.1.1 Classfcaton nto Maor Class Ths secton descrbes a method to classfy the gven roc mage nto one of the three types of rocs called gneous, metamorphc and sedmentary. The flowchart of classfcaton process s shown below. Fgure 3.1 Flowchart of roc mage classfcaton process usng GLCM. 3.1.1.1 Feature Extracton usng GLCM Matrx The analyss of surface of the roc mages s done to extract the promnent features. The feature extracton method consdered here s Gray level co-occurrence matrx. GLCM matrx s also nown as spatal gray level dependency SGLD) matrx. Statstcal methods use second order statstcs to model the relatonshps between pxels wthn the regon by constructng Spatal Gray Level Dependency SGLD) matrces. A SGLD matrx s the ont probablty occurrence of gray levels and for two pxels wth a defned spatal relatonshp n an mage. The spatal relatonshp s defned n terms of dstance d and angle θ. If the texture s coarse and dstance d s small compared to the sze of the texture elements, the pars of ponts at dstance d should have smlar gray levels. Conversely, for a fne texture, f dstance d s comparable to the texture sze, then the gray levels of ponts separated by dstance d should often be qute dfferent, so that the values n the SGLD matrx should be spread out relatvely unformly. Hence, a good way to analyse texture coarseness would be, for varous values of dstance d, some measure of scatter of the SGLD matrx around the man dagonal. Smlarly, f the texture has some drecton,.e. s coarser n one drecton than another, then the degree of spread of the values about the 2442 Vol. 6, Issue 6, pp. 2439-2447

man dagonal n the SGLD matrx should vary wth the drecton d. Thus texture drectonalty can be analyzed by comparng spread measures of SGLD matrces constructed at varous dstances d. From SGLD matrces, a varety of features may be extracted. The orgnal nvestgaton nto SGLD features was poneered by Haralc et al. Fgure 3.2 GLCM matrx But for smplcty of classfcaton process some of the 14 features are consdered. Those features are,. Angular second moment. Entropy. Contrast v. Correlaton v. Inverse dfference moment The defntons and formulae of those features are dscussed as follows.. ) ANGULAR SECOND MOMENT Angular Second Moment, also called energy and unformty, s a measure of textural unformty of an mage. Energy reaches ts hghest value when gray level dstrbuton has ether a constant or a perodc form. A homogenous mage contans very few domnant gray tone transtons, and therefore the P matrx for ths mage wll have fewer entres of larger magntude resultng n large value for energy feature. In contrast, f the P matrx contans a large number of small entres, the energy feature wll have smaller value. Angular Second Moment... 3.1).) ENTROPY Entropy measures the dsorder of an mage and t acheves ts largest value when all elements n P matrx are equal [3]. When the mage s not texturally unform many GLCM elements have a very small value, whch mples that entropy s very large. Therefore, entropy s nversely proportonal to GLCM energy. Entropy... 3.2).) CONTRAST Contrast s a dfference moment of the P and t measures the amount of local varatons n an mage. The contrast texture feature wll gve hgher values for the areas wth larger dfferences between pxels wthn each Gray-Tone Spatal-Dependence Matrces. Ths s smlar to the varance but s calculated a lttle dfferently. The contrast 2D scatter plot and mage as would be expected s smlar to varance. Untl contrast and varance are used n the classfcaton methods, the extent to whch these are smlar cannot be further descrbed here. Contrast P, )*log P, )),, 2 P, ) 3.3) 2443 Vol. 6, Issue 6, pp. 2439-2447

.v) CORRELATION Correlaton s a measure of mage lnearty. It shows how the pxel values n the mages are correlated. The correlaton feature descrbes whether the varatons n the pxel values are lnear or non- lnear. ) ) P, ) Correlaton...3.4) Where µ s mean and σ s standard devaton..v) INVERSE DIFFERENCE MOMENT Inverse dfference moment measures mage homogenety. Ths parameter acheves ts largest value when most of the occurrences n GLCM are concentrated near the man dagonal. IDM s nversely proportonal to GLCM contrast. IV. TOPIC MODEL GENERATION USING PROBABILISTIC LATENT SEMANTIC ANALYSIS PLSA) PLSA s a novel statstcal technque for the analyss of two mode and co-occurrence data, whch has applcatons n nformaton retreval and flterng, natural language processng, machne learnng from text, and n related areas. Ths PLSA method s based on a mxture decomposton derved from a latent class model. The PLSA model was orgnally developed for topc dscovery n a text corpus, where each document s represented by ts word frequency. The core of PLSA model s to map hgh dmensonal word dstrbuton vector of a document to a lower dmensonal topc vector. Therefore, PLSA ntroduces a latent topc varable between the document d d... d } and the word w w... w }. Then the { 1 n PLSA model s gven by the followng generatve scheme, 1. select a document p d 2. pc a latent topc 3. Generate a word, d wth probablty ) p z / d p w / z. z wth probablty ) w wth probablty) The model s graphcally shown n the fg D Z W { 1 m Fgure 4.1 PLSA model Ths PLSA model very well suts for the generaton of topc model from the Tamura features of the roc mages. Here the three types of roc are consdered as documents. The latent topc s the sub classes of rocs such as andeste, basalt, gabbro, coal, grante, amphboltes, and gness. The set of Tamura features calculated for the query mage and the tranng roc mages are consdered as words. Ths s depcted as follows. Document class of roc mage Latent topc subclass of roc mage Word feature of roc mage. As a result one obtan an observaton par d, w ) whle the latent topc varable s dscarded. Ths generatve model can be expressed by the followng probablstc model p w, d ) p d ) p w / d ) 4.1) Where p w / d ) K 1 p w / z ) z / d ) 2444 Vol. 6, Issue 6, pp. 2439-2447 p 4.2) The unobservable probablty dstrbuton p z / d ) and p w / z ) are learnt from the complete dataset usng expectaton maxmzaton EM) algorthm []. The log lelhood of the complete dataset s

L n d, w )log p d, w ) 4.3) N M 1 1 1 Where K n d, w )log p w / z ) p z / d ) 4.4) n d, w ) s value of the vsual word occurred n the word mage matrx n. Each row n the matrx represents an mage. The frst row corresponds to the features of the query mage and remanng rows are corresponds to the reference mages.e. tranng sequences. V. RESULT Roc name TABLE 6.4 SSD values for maor class CCM METHOD) Roc mage Igneous Andeste) Metamorphc Amphbolte) Sedmentary Brecca) Andeste 0 5.5696e+019 1.7095e+019 Basalt 1.5428e+019 1.2975e+020 4.2738e+016 Gabbro 3.0965e+018 3.2527e+019 3.4743e+019 Grante 2.1000e+019 8.2970e+018 7.5988e+019 Scora 9.6426e+017 4.2003e+019 2.6179e+019 Perdotte 5.2353e+017 6.7019e+019 1.1635e+019 Pumce 8.1885e+017 7.0021e+019 1.0431e+019 On comparng the results the Scora rocs gves the maxmum Igneous output of 9.6426e+017 and Basalt roc gves the maxmum Sedmentary output of 4.2738e+016 and the Grante roc gves the maxmum output of 8.2970e+018. VI. CONCLUSION Wth the rapd development of dgtal magng tools, magng applcatons have been adopted n many areas n whch nspecton and montorng have been done manually. The applcaton area of ths thess s an example of ths change. Formerly, roc and coal samples were nspected manually n the roc ndustry as well as n geologcal research. It was not recently far that magng and mage processng methods made t feasble to start developng automatc approaches for the vsual nspecton and recognton of rocs and coals. Compared to several other goods and materals that are nspected by computer vson systems on the producton lne, nspecton of roc materal s sgnfcant and more demandng analyss tas. In ths thess the methods and technques have been developed for the classfcaton of natural roc mages and segmentaton of group of coal mages. In mage classfcaton, vsual descrptors extracted from the mages are used to descrbe mage content. In ths proect, the vsual propertes of roc and 2445 Vol. 6, Issue 6, pp. 2439-2447

coal mages are extracted by usng Tamura features. In addton, a better topc model generaton process nown as PLSA s consdered. In GLCM and CCM methods the features are extracted and drectly appled to classfer. But here the PLSA model measures the probablty of co occurrence of partcular features beng wth varous types of roc mages. Snce ths s computatonally effectve and more effcent generatve model ths technque mproves the classfcaton accuracy compared to conventonal texture methods. The method ntroduced n ths thess s drectly applcable to practcal roc mage classfcaton and coal mage segmentaton problems. They can, therefore, be used whenever roc mage classfcaton systems are beng constructed. The accuracy obtaned n ths probablstc based method can be further mproved by usng better classfer called random forest classfer. VII. FUTURE WORKS In future we can enhance the mage to extract the features by usng the latest flterng methods le wavelet flterng, spatal doman flterng by usng medan flters and classfy the samples by usng support vector machne classfer. REFERENCES [1]. Automatc Roc Detecton and Classfcaton n Natural Scenes, 2006 Heather Dunlop. [2]. Color and Texture Based Classfcaton of Roc Images Usng Classfer Combnatons, 2006, LeenaLepsto. [3]. Correlated PLSA for Image Clusterng, 2010, PengL,Jan Cheng, Zechao L, and Hanqng Lu. [4]. Effect mage retreval based on hdden concept dscovery n mage database, 2007, Zhang, R.F., Zhang, Z.F. [5]. Evaluaton of texture methods for mage analyss, Mona Sharma, MarosMarou, Sameer Sngh. [6]. Fndng textures by textual descrptons, vsual examples, and relevance feedbacs, 2003, Hsn-ChhLn,Chh-Y Chu, Sh-Nne Yang. [7]. Image categorzaton va robust PLSA, 2010, Lu, Z.W, Peng, Y.X., Horace, H.S.Ip. [8]. Improvng the maxmum-lelhood co-occurrence classfer: a study on classfcaton of nhomogeneous roc mages, 2006 P.Pacl, S.Verzaov, R.P.W.Dun. [9]. Macroscopc Roc Texture Image Classfcaton Usng a Herarchcal Neuro Fuzzy Class Method, 2010 Laerco B. Goncalves and Fabana R. Leta. [10]. Probablstc Latent Semantc Analyss, Uncertanty n Artfcal Intellgence, UAI'99, Stocholm, Thomas Hofmann. [11]. Recent development n the use of co occurrence matrx for texture recognton, 2000, R.F. Waler, P.T. Jacway, I.D. Longstaff. [12]. Recent trends n texture classfcaton Jng Y Tou, Yong HaurTay, Phoo Yee Lau. [13]. Roc mage classfcaton usng color features n Gabor space, 2005 LeenaLepsto, IvarKunttu, Ar Vsa. [14]. Roc Image Classfcaton Usng Non-Homogenous Textures and Spectral Imagng, 2003, LeenaLepsto, IvarKunttu, JormaAuto, and Ar Vsa. [15]. Roc mage classfcaton based on - nearest neghbor votng, 2006,L. Lepsto, I. Kuntu, A, Vsa. [16]. Texture features for mage classfcaton. Robert M. Haralcet al. [17]. Roc Textures Classfcaton Based on Textural and Spectral Features Tossaporn Kachanubal, and Somat Udomhunsaul 2008. AUTHORS BIOGRAPHY R. Vnoth was born n Tamlnadu, Inda n 1985. He receved B.E from Mohamed Satha Engg College, Klaara, Ramanathapuram, Inda n the year 2007 and M.E. VLSI DESIGN) from Muthayammal Engg College, Raspuram, Inda n the year 2009. Hs area of nterest ncludes mage processng, Sgnal Processng. He s havng 5 years of teachng experence n the department of Electroncs and Communcaton Engg. He publshed few research papers n nternatonal ournals and presented few papers n natonal and nternatonal conferences. 2446 Vol. 6, Issue 6, pp. 2439-2447

R. Srnvasan was born n Tamlnadu, Inda n 1986. He receved B.E from Muthayammal Engg College, Raspuram, Inda n the year 2008 and M.E. Power Electroncs and Drves) from Paava Engg College, Raspuram, Inda n the year 20012. Hs area of nterest ncludes mage processng, Machne Drves. He s havng 2 years of teachng experence n the department of Electrcal and Electroncs Engg. He publshed few research papers n nternatonal ournals and presented few papers n natonal and nternatonal conferences. 2447 Vol. 6, Issue 6, pp. 2439-2447