FEATURE EXTRACTION. Dr. K.Vijayarekha. Associate Dean School of Electrical and Electronics Engineering SASTRA University, Thanjavur

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1 FEATURE EXTRACTION Dr. K.Vjayarekha Assocate Dean School of Electrcal and Electroncs Engneerng SASTRA Unversty, Thanjavur Jont Intatve of IITs and IISc Funded by MHRD Page 1 of 8

2 Table of Contents 1. FEATURE EXTRACTION Introducton Image texture Statstcal Feature Hstogram based features... 5 Fg. 4. Textural mages extracted from splttng nfected fruts... 5 Fg. 4.3 Textural mages extracted from stemend rot nfected fruts Cooccurrence matrx based features... 6 Jont Intatve of IITs and IISc Funded by MHRD Page of 8

3 1. FEATURE EXTRACTION 1.1 Introducton After an mage has been segmented nto regons of nterest, mage representaton and mage descrpton n a form sutable for further processng of the mage s very mportant. Representng a regon can be done n two ways. (1) n terms of ts external characterstcs and () n terms of ts nternal characterstcs. Choosng a proper representaton scheme makes the data useful. Based on the representaton method chosen, the regon needs to be descrbed. An external representaton s chosen when the prmary focus s on shape analyss. An nternal representaton s preferred when the prmary focus s on regonal propertes lke color and texture. The automatc gradng and sortng of ctrus fruts requres that the external surface defect be dentfed and classfed. The shape of the frut does not play a role n such an applcaton and so external representaton cannot be performed. Internal representaton s chosen for ths study, as the focus of the work s to dstngush the regon of the frut mage based on ether color or the texture. Even though color dfference between the normal surface and the defect surface s promnent, color alone wll not serve the purpose because, there wll always be varatons of color n natural products lke fruts and vegetables and may be of lttle use n classfcaton. Hence textural descrpton s preferred. The textural descrpton s done by extractng features from the mage. An mage feature s a dstngushng prmtve characterstc or attrbute of an mage. 1. Image texture Color s an mportant cue not only n human vson but also n dgtal mage processng where ts mpact s stll rsng. Color s measured globally accordng to the hstogram gnorng local neghborng pxels. Natural products lke fruts and vegetables do not have unform color throughout and has varatons between the samples pertanng to a sngle class. Images of natural scenes are devod of sharp edges over large areas. In these areas the scene can be characterzed as exhbtng a consstent structure analogous to the texture of cloth. Image texture measurements can be used to segment an mage and classfy ts segments. Texture s characterzed by the relatonshp of the ntenstes of neghborng pxels gnorng ther color. Texture plays an mportant role n many machne vson tasks such as surface nspecton, scene classfcaton, surface orentaton and shape determnaton. Texture s characterzed by the spatal dstrbuton of gray levels n a neghborhood. Jont Intatve of IITs and IISc Funded by MHRD Page 3 of 8

4 Texture s an mportant cue for the analyss of many mages. It s usually used to pont ntrnsc propertes of surfaces especally those that do not have a smoothly varyng ntensty. Several mage propertes such as smoothness, coarseness, depth, regularty etc. can be assocated wth texture. Texture can also be defned as a descrptor of local brghtness varaton from pxel to pxel n a small neghborhood through an mage. Texture can be descrbed as an attrbute representng the spatal arrangement of the gray levels of the pxels n a regon of a dgtal mage. It s often qualtatvely descrbed by ts coarseness and the coarseness ndex s related to the spatal repetton perod of the local structure. A large perod mples a coarse texture and small perod mples a fne texture. Texture s a neghborhood property of an mage pont. Therefore texture measures depend on the sze of the observaton neghborhood. Texture analyss has played an mportant role n many areas ncludng medcal magng, remote sensng and ndustral nspecton and mage retreval. The texture analyss s dverse and dffers from each other by the method used for extractng textural features. Four categores of extractng textural features are: 1) Statstcal methods ) Structural methods 3) Model based methods 4) Transformbased methods. Statstcal texture analyss technques descrbe texture of regons n an mage through hgherorder moments of ther grayscale hstograms. The most commonly used method for texture analyss s based on extractng varous textural features from a gray level cooccurrence matrx (GLCM). The GLCM approach s based on the use of secondorder statstcs of the grayscale mage hstograms. Structural texture analyss technques descrbe a texture as the composton of welldefned texture elements such as regularly spaced parallel lnes. The propertes and placement rules of the texture elements defne the mage texture. Model based texture analyss technques generate an emprcal model of each pxel n the mage based on a weghted average of the pxel ntenstes n ts neghborhood. The estmated parameters of the mage models are used as textural feature descrptors. Transform based texture analyss technques convert the mage nto a new form usng the spatal frequency propertes of the pxel ntensty varatons. The success of ths type les n the type of transform used to extract textural characterstcs from the mage. The mage processng of ctrus frut mages usng statstcal and transform based texture analyss s explaned here. Jont Intatve of IITs and IISc Funded by MHRD Page 4 of 8

5 Fg. 4.1 Textural mages extracted from pttng nfected fruts Fg. 4. Textural mages extracted from splttng nfected fruts Fg. 4.3 Textural mages extracted from stemend rot nfected fruts Except for the wavelet packet transform features, whch use the full frut mage for tranng and testng, all the other methods make use of cropped wndows contanng the surface defect regon. Features are extracted for the mages from mbank3, and stored as feature vectors. When classfcaton s performed for the full frut mage, the mage s cropped, features are extracted and classfcaton task s performed. Sample cropped wndows wth the three surface defects are shown n Fg, Fg and Fg. 1.3 Statstcal Feature One of the smplest approaches for descrbng texture s to use the statstcal moments of the gray level hstogram of the mage. The varous statstcal textural features are based on gray level hstogram, gray level cooccurrence matrx, and edge frequency and run length dstrbuton. In our research work, we concentrated only on frst and second order statstcs.e. gray level and cooccurrence based measures. 1.4 Hstogram based features The hstogrambased features used n ths work are frst order statstcs that nclude mean, varance, skewness and kurtoss. Let z be a random varable denotng mage gray levels and p(z ), Jont Intatve of IITs and IISc Funded by MHRD Page 5 of 8

6 =,1,,3,.L1, be the correspondng hstogram, where L s the number of dstnct gray levels. The features are calculated usng the abovementoned hstogram. (a) Mean The mean gves the average gray level of each regon and t s useful only as a rough dea of ntensty not really texture. (b) Varance L 1 m = z = p( z ) The varance gves the amount of gray level fluctuatons from the mean gray level value. µ L 1 ( z) = = ( z m) p( z ) (c) Skewness Skewness s a measure of the asymmetry of the gray levels around the sample mean. If skewness s negatve, the data are spread out more to the left of the mean than to the rght. If skewness s postve, the data are spread out more to the rght. L ( z m) ( z) = µ = (d) Kurtoss p( z ) Kurtoss s a measure of how outlerprone a dstrbuton s. It descrbes the shape of the tal of the hstogram. L ( z m) ( z) = µ = p( z ) 1.5 Cooccurrence matrx based features Measures of texture computed usng hstograms suffer from the lmtaton that they carry no nformaton regardng the relatve poston of the pxels wth respect to each other. One way to brng ths type of nformaton nto the texture analyss process s to consder not only the dstrbuton of the ntenstes but also the postons of pxels wth equal or nearly equal ntensty values. One such type of feature extracton s from gray level cooccurrence matrces. Jont Intatve of IITs and IISc Funded by MHRD Page 6 of 8

7 The secondorder gray level probablty dstrbuton of a texture mage can be calculated by consderng the gray levels of pxels n pars at a tme. A secondorder probablty s often called a GLC probablty. For a gven dsplacement vector D5 at Dx Dy, the jont probablty of a pxel at locaton (x, y) havng a gray level, and the pxel at locaton (x1dx, y1dy) havng a gray level j. In other words t s a secondorder jont probablty P (, j) of the ntensty values of two pxels ( and j), a dstance d apart along a gven drecton, whch s the probablty that j and have the same ntensty. Ths jont probablty takes the form of a square array P d wth row and column dmensons equal to the number of dscrete gray levels (ntenstes) n the mage beng examned. If an ntensty mage were entrely flat (.e. contaned no texture), the resultng GLCM would be completely dagonal. As the mage texture ncreases, the offdagonal values n GLCM become larger. The varous features that can be calculated from the cooccurrence matrces (C) are nerta (contrast), absolute value, nverse dfference, energy, and entropy. (a) Contrast Contrast s the element dfference moment of order, whch has a relatvely low value when the hgh values of C are near the man dagonal. contrast = ( j j) c j (b) Energy Energy value s hghest when all values n the cooccurrence matrx are all equal energy = c j j (c) Entropy Entropy of the mage s the measure of randomness of the mage gray levels. Entropy C j log C = j j The statstcal feature set consstng of seven feature vectors for each 4x4 sub wndow for the three surface defects s used. Table4.1 gves the statstcal features taken for three wndows of each type. Jont Intatve of IITs and IISc Funded by MHRD Page 7 of 8

8 Statstcal feature set S. No Surface Defect Mean Varanc e Skewnes s Kurtoss Contras t Energ y Entrop y 1 Pttng Pttng Pttng Splttng Splttng Splttng Stem end rot Stem end rot 9 Stem end rot Jont Intatve of IITs and IISc Funded by MHRD Page 8 of 8

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