Texture Recognition with combined GLCM, Wavelet and Rotated Wavelet Features

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1 International Journal of Computer an Electrical Engineering, Vol.3, No., February, Texture Recognition with combine GLCM, Wavelet an Rotate Wavelet Features Dipankar Hazra Abstract Aim of this paper is to evelop a texture recognition system for browsing an retrieval of image ata. Many features have been propose to precisely efine the natural texture properties. Tamura propose six features. Those features are coarseness, contrast, irectionality, line-likeness, regularity an roughness. Haralick extract some features from gray level co-occurrence matrix (GLCM). Gabor features an wavelet features are wiely use in image retrieval system an gives goo result. In this paper combination of gray-level co-occurrence matrix, Daubechies filters an rotate wavelet filters are use to get a high quality feature set. A new algorithm is propose to get rotate wavelet filter from Daubecheis wavelet coefficients. Experimental results emonstrate that the propose metho is very efficient an superior to some other existing metho. Inex Terms Content base image retrieval, rotate wavelet filter, texture retrieval, wavelet. I. INTRODUCTION With the large growth of igital image libraries searching an browsing images with the conventional keywor base techniques are becoming obsolete. To attach a keywor with the image is very ifficult task. Similarly using a keywor for searching is also ifficult. Hence there is requirement for Content Base Image Retrieval (CBIR). It helps in visual queries. Features of images are store as feature vector that is much smaller than original image. Image is searche by calculating minimum istance between feature vector of query image an same of the atabase image. Most powerful features use in Content Base Image Retrieval are shape, texture an color of the images. Texture base objects are those objects for which unlike shape base objects, there is no visible inter-object part-wise corresponence. These objects are better escribe by their texture than the geometric structure of reliably etectable part. Builings, roas, trees an skies are texture base objects. Sometimes when we are using color base segmentation shaow is etecte as ifferent object. But if we use texture base object etection technique shaow can be completely eliminate. For example, texture of vehicle is completely ifferent from texture of roa. Texture of roa oes not change when it is covere with shaow. Texture attribute of the roa backgroun is taken out. If texture attribute oes not change or little change then it is roa, otherwise it is vehicle. Many features have been propose to precisely escribe the natural texture properties. Tamura [] propose six Dipankar Hazra is a Senior Lecturer in the epartment of Computer Science & Engineering, Dr..B.C.Roy Engineering College, Fuljhore, Durgapur, West Bengal, Inia.( ipankar998@reiffmail.com). 46 texture features namely coarseness, contrast, irectionality, linelikeness, regularity an roughness. When two patterns iffer only in scale the magnifie one is coarser. For texture of ifferent structure the bigger is the element size, less the elements are repeate is calle coarser. Contrast can be seen as ynamic range of gray level or sharpness of eges etc. Directionality can be monoirectional or biirectional. It is inepenent of orientation. Line-likeness is concerne only with shape of the texture element. Regularity is the variation of the property rule. Roughness is the total energy changes in the gray levels. It epens on coarseness an contrast. Many texture base feature extraction methos are possible. Some of the most popular methos are gray level co-occurrence matrix [], [3], gabor filters [4], haar filters [5], aubechies filters [ 6]-[9] etc. In this paper combination of gray level co-occurrence matrix, aubechies filters an rotate wavelet filters [7] have been use as feature extraction methos. II. RELATED WORKS A. Gray Level Co-occurrence Matrix Distribution of pixel gray levels can be escribe by secon-orer statistics like the probability of two pixels having particular gray levels at particular spatial relationships. This information can be summarize in two imensional gray level co-occurrence matrices, which can be compute for various istances an orientations. In orer to use information containe in the GLCM, Haralick efine some statistical measures to extract textual characteristics. Some of these features are entropy, contrast, homogeneity, correlation etc. Contrast measures the local variation in the gray level of glcm. Correlation measures the joint probability of occurrence of pixel pairs of glcm. Energy gives the sum of square pixel values of glcm. Homogeneity refers to the closeness of istribution of elements to the glcm iagonal. Homogeneous textures contain ieal repetitive structures. Weak homogeneity refers to variation in texture elements in their spatial arrangements. The GLCM element C(i, j,, Ө) represent probability of the pair of pixels, which are locate with an inter sample istance an a irection Ө, have a gray level i an a gray level j. The feature use in this work is Homogeneity. If the inter-pixel istance is set to or glcm measure the local high frequency information. B. D Wavelet A wavelet is a mathematical function that is use to represent a continuous time signal into ifferent scale components. A wavelet transform is representation of a

2 International Journal of Computer an Electrical Engineering, Vol.3, No., February, function by wavelets. It has avantages over traitional Fourier transform for representing signal with iscontinuities an sharp peaks an for accurately econstructing an reconstructing signals. The most known family of orthogonal wavelets is a family of Daubechies. Daubechies wavelets are useful for texture classification an more popular ue to their relations to multiresolution analysis (MRA). Daubechies wavelets are usually enominate by the number of nonzero coefficients a_k, so usually talk about Daubechies 4, Daubechies 6 etc. wavelets. Roughly sai, with the increasing number of wavelet coefficient the functions become more smooth. Reference [6] shows that if φ() t is the scaling function an ψ() t is the wavelet function then φ() t satisfies the following: It integrates to. Φ () tt= () The scaling function has unit energy. Φ ( t) t = () The set consisting of φ () t an its integer translates are orthogonal. < Φ(), t Φ( t n) >= δ( n) (3) Wavelet function ψ() t satisfies the following: It integrates to. ψ () tt = (4) The wavelet function has unit energy. ψ ( t) t = (5) The set consisting of ψ() t an its integer translates are orthogonal. < ψ (), t ψ ( t n) >= δ ( n) (6) The set consisting of ψ () t an φ() t or its integer translates are orthogonal. < ψ (), t Φ ( t n) >= (7) If Daubecheis 4 scaling function coefficients are h, h, h, h 3 an wavelet function coefficients are g, g, g, g 3 then we obtain the following equations: h + h + h + h = (8) 3 4 hh hh 3 + = (9) h + h+ h + h3 = () g + g + g + g = () 3 gg gg 3 + = () g + g + g + g = (3) 3 g + g + 3g = (4) 3 Equation (8) an Equation (9) correspon to orthonormality of scaling functions. Equation () satisfies ilation equation. Similarly Equation () an Equation () correspon to orthonormality of wavelet function. Equation (4) gives moment of wavelet function. Solving the above equations we get following coefficient values: h = (+ 3) / 4 h = (3 + 3) / 4 h = (3 3) / 4 h 3 = ( 3) / 4 Since wavelet function is orthogonal to scaling function, scaling function an wavelet function coefficients are relate as following: g= h3, g= h, g= h, g3= h The image is ivie into 4 subbans by applying DWT as shown in Fig. (a). These subbans are labele LH, HL, an HH represents finest scale wavelet coefficients while LL correspons to coarse level coefficient. LL further ecompose to get next coarse level of wavelet coefficients. This results into two level wavelet ecomposition as shown in Fig. (b). This process is continue until some final scale is reache. LL LL LH HL LH HH LH C. Rotate Wavelet Filter HL HH Fig. (a) -level Image Decomposition HL HH Fig. (b) -level Image Decomposition In texture retrieval application characterization of irectional information improves texture retrieval performances. Rotate iscrete wavelet filters can be use by rotating stanar D iscrete wavelet filters. High-High subban in stanar DWT ecomposition contains the iagonal information of texture image. It is ifficult to istinguish whether it is 45 or 35. Hence rotate wavelet filter has been use. Here Daubecheis 4 filter coefficients are oriente at angle Ө an convolve with the texture image at multiple resolutions. The value of Ө can be to 8 to retrieve texture information on that specific 47

3 International Journal of Computer an Electrical Engineering, Vol.3, No., February, irection. In this experiment Ө has been taken as 45. Low-High an High-Low subbans are mainly containing iagonal information. Hence only energy of these two subbans at multiple resolutions has been consiere. Texture Database Input Image III. PROPOSED APPROACH A. Texture Recognition System Fig. illustrates the propose Texture Recognition System. This system is compose of two sub-systems or components: (i) texture atabase (ii) texture recognition function Texture Database: Each texture name, texture type an glcm feature vector is store in a table for glcm texture atabase. Similarly, each texture name, texture type an D wavelet feature vector an rotate wavelet filter feature vector is store in a table for wavelet texture atabase. Texture Recognition Function: This function calculates an stores the feature vectors of training images. It also calculates feature vectors of the query image an compares with the feature vectors of training images. It classifies the query image. This function consists of two sub function. One is for gray level co-occurrence matrix an another is for stanar wavelet an rotate wavelet filter. B. Texture Features For GLCM Homogeneity are calculate for four irection (i.e. Ө=, 45, 9 or 35 ). A feature vector of size 4 is create for the image. Homogeneity is compute as follows, (, ) Homogeneity = p i j + i j (5) i, j The DWT ecomposition of the image is performe up to 5 th level. Energy for each level, for each sub ban (High-High, High-Low, Low-High, Low-Low) are calculate an taken as feature vector of size 5 4=. Energy is compute as follows, M N Energy = Xij M N (6) i = j = Similarly RWF ecomposition of the image is performe up to 5 th level. Energy for each level, for Low-High an High-Low subban are calculate to extract the iagonal texture features using the same formula above. This gives feature vector of size 5 =. Fig. Propose Texture Recognition System C. RWF Algorithm Following algorithm has been use to orient Daubechies 4 coefficients at angle Ө for creating rotate wavelet filter. Let lpf an hpf represent the one imensional Daubechies 4 low pass an high pass filter coefficients respectively. lpf= [h h h h 3 ], hpf= [g g g g 3 ]. Let rot_lpf an rot_hpf represent the rotate wavelet lowpass an high pass filters coefficients respectively. In the propose algorithm the rot_lpf an rot_hpf using Ө=45 will be as following: rot lpf= Retrieve Feature Vector Calculate Feature Vector Calculate istance between feature vectors of Query image an Database Image Calculate minimum istance & classify h h h h 3 rot hpf= g g g g 3 Step I: Set lpf to Daubechies 4 scaling coefficients; 48

4 International Journal of Computer an Electrical Engineering, Vol.3, No., February, Step II: Step III: Step IV: Step V: lf= length(lpf); FOR to lf- COMPUTE hpf(i+) = (-)^i * lpf(lf-i) END FOR; SET Ө to rotation of wavelet filter; COMPUTE m=tangent(п/- Ө); Step VI: IF (m is less than ) SET x to ; SET y to m; ELSE SET y to ; SET x to /m; END IF; Step VII: SET xa to ; SET ya to ; SET rot_lpf(xa,ya) to lpf(); SET rot_hpf(xa,ya) to hpf(); Step VIII: FOR i to (lf-) COMPUTE xa=xa+x; COMPUTE ya=ya+y; COMPUTE rxa=roun(xa); COMPUTE rya=roun(ya); SET rot_lpf(rxa,rya) to lpf(i+); SET rot_hpf(rxa,rya) to hpf(i+); END FOR; D. Texture Recognition The feature vector of the query image is compare with the feature vector of the atabase image. It then classifies the image accoring to minimum istance. Let x an y are feature vectors of two images. x = [x x x 3 x ] y = [y y y 3.y ] Canberra Distance is canb ( x, y) = xi yi xi+ yi Eucliean Distance is mostly use. Each imension is square before summation in Eucliean istance metho. It is more useful when issimilarity is large [7], whereas Canberra Distance appears to be goo similarity measure to be use which normalizes the ifference to avoi scaling effect. IV. EXPERIMENTAL RESULT The images are obtaine from the Broatz image atabase []. Experiments are conucte with 5 types of images with 7 orientations (, 3, 6, 9,, 5 & ). sub image of size are obtaine from each image to form the texture image atabase. Texture Database consists of the following atasets. Water Dataset: 7 textures have been taken as San Dataset: 7 textures have been taken as training ataset. Grass Dataset: 7 textures have been taken as Woo Dataset: 7 textures have been taken as Pigskin Dataset: 7 textures have been taken as Fig.3 shows sample texture images of each type. 5 types of images with 7 orientations with size have been consiere as query image. Hence total numbers of query images are 5 7=35. Fig.4 shows similar most images for a query image. Water..tiff is an image of water with orientation. A subimage of size of water..tiff has been use as the query image with top left coorinate at (57,57). Following the query image, there are similar images of size Image name an the co-orinate of the top left corner of that image are specifie below each image. From Table I it is observe that combination of GLCM, Daubechies 4 Wavelet, an Rotate Wavelet outperforms other two methos. But retrieval accuracy of ifferent textures is ifferent. This is shown in Table II. x,x,x 3,., x are energy of ifferent subbans ( LL, LH, HL, HH) of ifferent levels of wavelet ecomposition. Similarly, y,y,y 3,.,y are energy of ifferent subbans ( LL, LH, HL,HH) of ifferent levels of wavelet ecomposition. Distance between x, y is as following: Manhattan Distance Water..tiff San.3.tiff Grass.9.tiff isman( x, y) = xi yi Woo..tiff Pigskin..tiff Eucliean Distance iseucl( x, y) = ( xi yi) Fig.3 Sample Texture images of size use in our work Query Image 49

5 International Journal of Computer an Electrical Engineering, Vol.3, No., February, (water..tiff, 57,57) Top similar images (water.5. (water.3. (water.. tiff,,57) tiff,9,93) tiff, 9,385) (water.5. (water.. (water.. tiff, 9,9) tiff, 9,385) tiff, 9,93) (water.5. (water.5. (water.. tiff,9,385) tiff,,385 ) tiff, 9,3) REFERENCES [] H Tamura, S Mori, T Yamawaki, Textural Features Corresponing to Visual Perception, IEEE Transaction on Systems, man an Cybernetics, Vol. 8, No. 6, 978W.-K. Chen, Linear Networks an Systems (Book style). Belmont, CA: Wasworth, 993, pp [] R.M. Haralick, K. Shanmugam, I. Dinstein, Textural Features for Image Classification, IEEE Transaction on Systems, man an Cybernetics, Vol. SMC-3, pp.6-6, 973.B. Smith, An approach to graphs of linear forms (Unpublishe work style), unpublishe. [3] Shankar Bhausaheb Nikam, Suneeta Agarwal, Wavelet Energy Signature an GLCM Features-base fingerprint anti-spoofing, Proceeings of the 8 International Conference on Wavelet Analysis an Pattern Recognition, Hong Kong, 3-3 Aug,8. [4] Manjunath B.S, Ma W.Y, Texture features for browsing an retrieval of image ata, IEEE Transactions on Pattern Analysis an Machine Intelligence, Vol. 8, No. 8, Aug,996. [5] Q Tian, N Sebe, M S Lew, E Loupias, T S Huang, Image Retrieval using wavelet-base salient points, Journal of Electronic Imaging, Special Issue on Storage an Retrieval of Digital Meia, pp , vol.(4),oct,. [6] Raghuveer M. Rao an Ajit S. Boparikar, Wavelet Transforms: Introuction to Theory & Application, Pearson Eucation, Inia, 9, pp [7] M. Kakore, P.K. Biswas, B.N. Chatterjee, Texture image retrieval using rotate wavelet filters, Pattern Recognition Letters 8, 4-49, 7. [8] K. Muneeswaran, L. Ganeshan, S.Arumugam an K. Ruba Sounar, Texture classification with combine rotation an scale invariant wavelet features, Pattern Recognition 38,495-56, 5. [9] Tianhorng Chang an C C Jay Kuo, Texture Analysis & Classification with Tree-Structure Wavelet Transform, IEEE Transaction on Image Processing, vol-, no 4, 993. [] P. Broatz, Textures: A Photographic Album for Artists an Designers, Dover, New York, 966. (water.3.tiff, 9,57) Fig.4 Query image an similar most images of query image of size Dipankar Hazra receive the B.C.S.E. egree an M.TECH IT (Courseware Engineering) egree both from Jaavpur University in 998 an 9 respectively. He is a Senior Lecturer at the Department of Computer Science an Engineering at Dr.B.C.Roy Engineering College, Fuljhore, Durgapur,West Bengal, Inia. His research interests inclue DBMS, Data Mining an Image Processing. TABLE I ACCURACY TABLE FOR DIFFERENT METHOD. Daubechie s 4 Wavelet Daubechies 4 Wavelet & Rotate Wavelet GLCM & Daubechies 4 Wavelet & Rotate Wavelet 65.7% 77.4% 85.7% Table II Accuracy Table for Different Textures using GLCM, Daubechies 4 Wavelet an Rotate Wavelet. Woo Grass Water San Pigskin % % 85.7% 85.7% 57.4% V. CONCLUSION In this research an efficient texture base object recognition algorithm is propose. The result shows that the approach has given goo result comparing to other existing texture recognition algorithm. We have use energy feature of wavelet subbans to classify image. Further research can be one to improve retrieval accuracy by using other features of wavelet subbans. Length of the wavelet filters can also be increase to get better result. Also the propose Rotate Wavelet Filter algorithm can be applie for other problems like noise removal of an image. 5