Multi Features Content-Based Image Retrieval Using Clustering and Decision Tree Algorithm

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1 TELKOMNIKA, Vol.4, No.4, Deember 06, pp. 480~49 ISSN: , aredited A by DIKTI, Deree No: 58/DIKTI/Kep/03 DOI: 0.98/TELKOMNIKA.v4i Multi Features Content-Based Image Retrieval Using Clustering and Deision Tree Algorithm Kusrini Kusrini*, M. Dedi Iskandar, Ferry Wahyu Wibowo 3 STMIK AMIKOM Yogyakarta, Jl. Ringroad Utara Condong Catur Depok Sleman Yogyakarta Indonesia, Telp: /fax: *Corresponding author, kusrini@amikom.a.id, dedi.s.kom@gmail.om, ferry.w@amikom.a.id 3 Abstrat The lassifiation an be performed by using the deision tree approah. Previous researhes on the lassifiation using the deision tree have mostly been intended to lassify text data. This paper was intended to introdue a lassifiation appliation to the ontent-based image retrieval (CBIR) with multiattributes by using a deision tree. The attributes used were the visual features of the image, i.e. : olor moments (order, and 3), image entropy, energy and homogeneity. K-means luster algorithm was used to ategorize eah attribute. The result of ategorized data was then built into a deision tree by using C4.5. To show the onept in appliation, this researh built an appliation with main features, i.e.: ases data input, ases list, training proess and testing proess to do lassifiation. The resulting tests of 50 rontgen data showed the training data lassifiation s truth value of 75.33% and testing data lassifiation of 55.7%. Keywords: Image, Classifiation, Deision Tree, Clustering Copyright 06 Universitas Ahmad Dahlan. All rights reserved.. Introdution Image database has been ommonly used by many appliation domains nowadays suh as multimedia searh engines, digital libraries, medial databases, geographial databases, e-ommere, online tutoring systems and riminal investigations. Image database ould be visualized using image browsing as a way to retrieval systems []. Image retrieval an be performed using attributes attahed to the image suh as reation date, storage loation, size or other predefined attributes. However, the searh performane produed in this way is highly depended on the expertise of a user in desribing the image. In addition, the searh ould not be performed based on the semantis of the image itself []. Tehnology is evolving towards image searhing by using method alled ontent-based image retrieval (CBIR) and also known as query by image ontent (QBIC) [3]. Instead of taking image s information from external resoures or metadata, CBIR approah used the intrinsi features of the image suh as olor, shape, texture, or a ombination of these feature elements [4]. Color as an image feature has been suesfully applied in image retrieval appliation sine it has strong orrelation with the objets inside the image [5]. Furthermore, the olor feature s robustness has been proven in proessing saled and orientation-hanged images [6-0]. The image itself is lassified into three images i.e. intensity image, indexed image, and binary image []. To be more speifi, CBIR s retrieval tehnique is based on information extrated from pixels [, 3]. CBIR has been widely applied in many researh projets. One of appliation of CBIR is for reognizing porn image [4-5]. CBIR is also used in health area researhes. It is used as image administration system that supports the physiian task suh as diagnosis, telemediine, teahing and learning new medial knowledge [6-8]. This paper introdues the onstrution of CBIR by using a ase based reasoning onept. In previous researh, ase-based reasoning has been explained as a onept to build rule for linial servie, speifially in diagnosis problem domain [9]. One algorithm that an be used to build rules as a deision tree is C4.5. It ould be also aplied to lassify image [0-]. The novelty of this researh is the ombining used of the lustering method and deision tree developing in image lassifiation. Instead of predetermining the disrete data Reeived August 8, 06; Revised Otober, 06; Aepted Otober 6, 06

2 TELKOMNIKA ISSN: manually for lassifiation proess, the data lustered by using the k-means lustering method. This proess is used to lassify eah of image features before lassifiation proess.. Researh Method.. Arhiteture System The arhiteture system of CBIR in this paper was formulated as shown in Figure. All of image douments were preproessed in order to make all images have the same format and size. After the preproessing step have finished, some visual features were then extrated from the images and stored into image databases. In the next step, using the k-means lustering method [3], eah of the features was then ategorized and stored into ategorized image database. These data were then used to build the deision tree using the C4.5 algorithm. Figure. CBIR Arhiteture.. Image Feature Extration Images were preonditioned into some same states, before the proess of feature extration was started, namely size of 40x40 pixel, bmp format, and turned into 8 bit grey image olor mode. The features used in this experiment are quite similar to previous study, that are olor moment and texture. The olor moment was divided into 3 low-order moments while the texture features used were ontrast, orrelation, energy, homogeneity, and entropy [4], but in this paper used entropy, energy, ontrast, and homogeneity [5]. Entropy, energy, ontrast, and homogeneity features represent image texture. Texture an be defined as a region s harateristi that is wide enough to form pattern repetition. In another definition, texture is defined as a speifially ordered pattern onsisting of pixel strutures in the image. One parameter needed to alulate the value of entropy, energy, ontrast, and homogeneity is obtained from o-ourene matrix, that is a matrix that desribes the frequeny with whih a pair of two pixels with a ertain intensity within a ertain distane and diretion ours in the image. Co-ourrene intensity matrix p (i, i) is defined by the following two simple steps:. Calulate the distane of every two dots in the image, expressed in a vetor of vertial and horizontal diretions (d = (dx, dy)). The values of dx and dy are also in pixel. Multi Features Content-Based Image Retrieval Using Clustering and (Kusrini Kusrini)

3 48 ISSN: Count the number of pixel pairs that have intensity values of i and i and distane of d pixels. Put the alulation of eah pair intensity values into the matrix aording to the oordinates, whih the absissa for intensity value i and the ordinate for intensity value i. Entropy is an image feature used for measuring the disorder of intensity distribution. The entropy alulation is shown in Equation. Entropy i, ) i p( i, ) log ( i p i i () Energy is a feature for measuring the number of onentration of intensity pairs in oourrene matrix. Equation is used to alulate this feature s value. The value will be inreased if qualified pixel pairs that mathed the o-ourrene intensity matrix requirement are more onentrated at some oordinates of the matrix and will be dereased if they are more dispersed. Energy i p i (i, i ) () The ontrast feature is used to measure the strength of different intensity of the image, while homogeneity is used to measure homogeneity of image intensity variation. Image s homogeneity value will be inreased as its variation intensity is dereased. The formula to measure the image ontrast is shown in Equation 3 while that for measuring the homogeneity is shown in Equation 4. Contrast i (i i ) i p(i, i ) (3) p(i Homogeneity, i ) i i i i (4) The notation of p in Equation, 3 and 4 denotes the probability and has value in the range of 0 to, that is the element value in o-ourrene matrix. Meanwhile i and i are denoted as nearby intensity pair in the x and y diretion. Given a sample of -bit image data with a size of 3 x 3 pixels. Finding the mean of RGB values of eah pixel, the result is represented in a matrix as shown in Figure. Figure. Image s RGB mean matrix Feature alulations of entropy, energy, ontrast and homogeneity is based on the oourrene intensity matrix. Consequently, prior to searhing those features values, oourrene intensity matrix is needed to be built. In this system, the value of distane (d)= and diretion in angle 45 o (dx= and dy=) are predetermined. From the above setting, then the value of o-ourrene intensity matrix value from image matrix Figure is obtained as shown in Figure 3. The seond value in row dx = and olumn dy = is obtained from pair ount with distane and angle 45 o between intensity value of image and intensity value of image as illustrated on Figure 4. TELKOMNIKA Vol. 4, No. 4, Deember 06 :

4 TELKOMNIKA ISSN: Figure 3. Co-ourrene Intensity Matrix Figure 4. Illustration of pair ount alulation The value of p(0,) is obtained from element value on row dx=0 and olumn dy= divided by total element value in o-ourrene matrix. Thus, the value of p(0,) is ¼ or 0.5. The entropy visual feature value is alulated by Equation, as follows: entropy =-((p(0,0)log(p(0,0)+(p(0,)log(p(0,)+(p(,0)log(p(,0)+ (p(,)log(p(,) =-(0.5 log(0,5)+0.5 log(0.5)+0 log(0)+0.5 log(0.5) =-( ) =0.45 The value of energy visual feature is alulated by Equation, as follows: energy = ( p (0,0) p (0,) p (,0) p (, )) = ( ) = is: The value of visual feature ontrast is obtained with alulation using Equation 3. That ontrast = ( 0 0) 0.5 (0 ) 0.5 ( 0) 0 ( ) 0. 5 = = 0.5 Visual feature homogeneity is obtained with alulation using Equation 4. That is: Homogeneity = = = Color moment an be defined as simple representation of olor feature in olored images. The three low-order moments of olor for apturing the information of image s olor distribution are mean, standard deviation and skewness [5]. For a olor in the image, the mean of is symbolized as μ, the standard deviation as σ and the skewness as θ. The values of μ, σ, and θ are alulated using Equation 5, Equation 6, and Equation 7, respetively. M N p ij (5) MN i j M N ( ) p i j ij MN (6) M N 33 ( p ij ) MN i j (7) Multi Features Content-Based Image Retrieval Using Clustering and (Kusrini Kusrini)

5 484 ISSN: Where M and N are the horizontal and vertial sizes of the image and is the value of olor in the image s row i and olumn j. Using Equation 5, the value of visual features olor moment order is alulated from the matrix as follows: = (0 0 ) 3x3 = 0.78 The value of olor moment order is obtained using the following steps:. Transform eah value in the RGB mean matrix in Figure by operating eah value in the matrix with the Equation 8. nb ( p ij ) (8) pij is the matrix ell s value in row i and olumn j and nb is the new value of the ell. The result is then represented in Figure 5. Figure 5. Color Moment Order Value Matrix Figure 6. Color Moment Order 3 Matrix. The olor moment order value is alulated from the matrix using Equation. M N = nb MN i j =.556 3x3 = 0.46 The olor moment order 3 value is obtained with the following steps:. Transform eah value in the RGB mean matrix in Figure by operating eah value in the matrix with the Equation 9. nb 3 ( p ij ) (9) pij is the matrix ell s value in row i and olumn j and nb is the matrix new ell s value. Using these alulations, the matrix in Figure will be transformed into the olor moment order 3 matrix as shown in Figure 6.. The olor moment order 3 value is alulated from the matrix using Equation 3. M N 3 nb MN i j TELKOMNIKA Vol. 4, No. 4, Deember 06 :

6 TELKOMNIKA ISSN: = x3 = K-Mean Clustering Clustering mahine is intended to map the ontinous or disrete lass data with many variations into disrete lass with determined variations. The input of this proess is:. Initial data, that an be either ontinuous or disrete data.. Delta, that is the value to be used to determine the allowed gap between entroid and mean. The output of this proess is a mapping table ontaining the disrete lass inluding its entroid value. The algorithm used in the data lustering mahine is derived and extended from k- means lustering algorithm. This data lustering using the K-Means method is ommonly done with basi algorithm as follow [6]:. Determine ount of lass (luster). Determine initial entroid of eah lass 3. Put eah data into lass whih has the nearest entroid 4. Calulate the data mean from eah lass 5. For all lass, if the differene of the mean value and entroid goes beyond tolerable error, replae the entroid value with the lass mean then go to step 3. In fundamental k-means lustering algorithm, initial entroid value from speifi disrete lass is defined randomly, while in this researh the value is produed from an equation shown in Equation 0. ( i ) * (max min) (max min) i min (0) n * n Where, i : entroid lass i min : the lowest value of ontinue lass data max : the biggest value of disrete lass data n : total number of disrete lass The building proess of disrete lass is shown as follows: ) Speify the soure data; ) Speify desired total number of disrete lass (n); 3) Get the lowest value from soure data (min); 4) Get the highest value from soure data (max); 5) Speify delta (d) to get the aeptable error by Equation ; 6) For eah disrete lass, find the initial entroid () by Equation 0; 7) For eah value in soure data, put it into its appropriate disrete lass, whih is one that has nearest entroid to the value; 8) Calulate the average value of all members for eah lass (mean); 9) For eah disrete lass, alulate the differene between its mean and its entroid (s) by Equation ; 0) For eah disrete lass, if s>e, then replae its entroid value with its mean, put out all values from their orresponding lass then go bak to step 7. e d *(max min) () s n i mean i i ().4. C4.5 Algorithm Deision tree is one of methods in lassifiation proess. In deision tree, lassifiation proess involving lassifiation rules generated from a set of training data. The deision tree will split the training data based on some riteria given. The riteria are divided into target riteria and determinator riteria. The lassifiation proess produes the target riteria values based on determinator riteria values [7]. The algorithm of deision tree building will searh for the best deiding riterion for ategorizing data homogenously. The riterion will be onsidered as root node. The next step of Multi Features Content-Based Image Retrieval Using Clustering and (Kusrini Kusrini)

7 486 ISSN: the algorithm is reating branhes based on the values inside of the riterion. The training data then ategorized based on values at the branh. If the data onsidered homogen after the previous step, then the proess for this branh is immediately stopped. Otherwise, the proess will be repeated by hoosing another riterion as next root node until all the data in the branh are homogen or there is no remain deiding riterion an be used. The resulting tree will then be used in lassifiation of new data. The proess of lassifiation is onduted by mathing the riterion value of the new data with the node and branh in deision tree until finding the leaf node whih is also the target riterion. The value at that leaf node is being the onlusion of lassifiion result. Using C4.5 algorithm, hoosing whih attribute to be used as a root is based on highest gain of eah attribute, the gain is ounted using Equation 3 and 4 [8]. n S i Gain( S, A) Entropy( S) * Entropy( Si) (3) i S Entropy( S) n pi * log pi i (4) Where S : Case set A : Attribute n : Partition Count of attribute A Si : Count of ases in i th partition S : Count of ases in S : Proportion of ases in S that belong to the i th partition p i 3. Results and Analysis 3.. Result The result of this researh is a omputer program to lassify images. The program s main features are ) Data Case Input; ) Case List; 3) Training Proess; and 4) Testing Proess. 3.. Data Input and Case List The Data Case Input feature is used to store images that will be used in the training proess. In this feature, the program will do preproessing, take the image visual feature s values and then store them in the database. The ase data input feature interfae is shown in Figure 7. This figure shows that the loaded image proessed to some visual features as explained in setion. and the lassifiation text box is filled manually as the expert judgement. Figure 7. New Case Input Feature TELKOMNIKA Vol. 4, No. 4, Deember 06 :

8 TELKOMNIKA ISSN: The Case List feature is used to review the ases stored in the database. This feature is also provided with ability to delete a partiular data that are assumed not to be used in the next training proess. The Case List page is shown in Figure 8. Figure 8. List of Cases Feature 3.. Training Proess using K-Means Cluster and C4.5 Algorithm The training proess is used to produe the deision tree from the ases in the ase list. This proess ontains sub proesses, they are ategorization proess and deision tree building proess. The ategorization proess is performed for eah of image visual feature by using K-Means Cluster algorithm as explained in setion.3. The input of ategorization proess is all values of images in a features and the result is the luster and the entroid of eah data. The luster seleted will be the one that has the nearest entroid. The deision tree building proess is performed using C4.5 algorithm. The training proess is then visualized in the form of deision tree as in Figure 9 and in a table of rule as shown in Figure 0. Figure 9. Tree Feature Multi Features Content-Based Image Retrieval Using Clustering and (Kusrini Kusrini)

9 488 ISSN: Figure 0. List of Rules Feature 3.3. Testing Proess The testing feature is used to do lassifiation to newly inoming images. The lassifiation is arried out by omparing between the new image s visual feature value and the rule onstruted in the prior training proess. The testing feature is shown in Figure. Figure. Testing Feature 3.. Analysis The testing proess was arried out with 50 rontgen image data and resulted some lassifiations. The data is shown in Table. Table. Cases Classifiation Classifiation Count Bakbone 50 Chest 50 Head 50 TELKOMNIKA Vol. 4, No. 4, Deember 06 :

10 TELKOMNIKA ISSN: The testing was arried out by omparing between the lassifiation from system s output and the expert judgment in Case Input Data as explained in setion 3.. The testing to all training data resulted to true lassifiation for 3 data. It means 75.3% of truth value. The detail result of testing training data is shown in Table. Table. Testing Result of All Training Data Classifiation Testing Result Count Bakbone Bakbone True 30 Bakbone Chest False Bakbone Unlassified Unlassified 8 Chest Bakbone False 0 Chest Chest True 34 Chest Head False Chest Unlassified Unlassified 5 Head Chest False Head Head True 49 Beside the testing proess to all training data, it is also onduted 0 experiments using 0 data. The data onsists of 40 Bakbone, 40 hest, and 40 head image data with different ombination. The test result is shown in Table 3. Table 3. Testing Results of the 0 Training Data Iteration True False Unlassified Average % The testing to testing data was done by 0-fold ross validation. It is used 0 training data and 30 testing data for eah of experiments. The test result with this model is shown in Table 4. From the Table 4, it is known that the testing proess to the data test produing orret lassifiation of 55.7% wrong lassifiation of 33.7% and unlassified data about 0.7%. Table 4. Testing Results of the Test Data Iteration True False Unlassified Average % Multi Features Content-Based Image Retrieval Using Clustering and (Kusrini Kusrini)

11 490 ISSN: In order to ompare the result, the same dataset is tested into lassifiation algorithm without lustering preproessing. This time, the lass range is defined manually. The testing to all training data resulted to true lassifiation for 3 data. It means 5.3% of truth value. The result of 0 experiments using 0 training data with different ombination without lustering proess is shown in Table 5. Table 5. Testing Results of the 0 Training Data Without Clustering Proess Iteration True False Unlassified Average 8,4 7,6 84 % 5,3 4,7 70 The result of testing to testing data that was done by 0-fold ross validation that is not using lustering proess. The result is shown in Table 6. From the Table 6, it is known that the testing proess to the data test without lustering proess produing orret lassifiation of 7% wrong lassifiation of 6% and unlassified data about 67%. Table 6. Testing Results of the Test Data Without Using Clustering Proess Iteration True False Unlassified Average 5, 4,8 0, % Comparing the above proesses of lassifiation with and without lustering algorithm, it shows that the using of lustering an inrease the auray of lassifiation proess. Sine it is done manually, the lassifiation without lustering algorithm is less aurate to define the lass range based on data distribution. 4. Conlusion From the disussion setion above, it an be onluded that: () The use of deision tree an be applied to lassify image with multi visual features, and () Clustering algorithm an help to improve ategorization proess in eah of this researh s seleted image visual features. The auray of the proess using the training data reahed 75.33%. The auray of the proess using the testing data reahed 55.7%. It is higher than lassifiation without using lustering proess that only reahed 5.3% for training data and 7% for testing data. TELKOMNIKA Vol. 4, No. 4, Deember 06 :

12 TELKOMNIKA ISSN: Based on the onlusion, there are some opportunities and hallenges for further researhes to improve this researh, among others determining the lassifiation of data whih are still in unlassified ategory in order to get a better auray value and examining other visual features that are able to inrease lassifiation auray value. Referenes [] Shaefer G. Interative Browsing of Large Image Databases. IEEE Conferene - Sixth International Conferene on Digital Information Proessing and Communiations (ICDIPC). Beirut. 06: [] Karmakar S, Dey S, Sahoo S. Towards Semanti Image Searh. IEEE Tenth International Conferene on Semanti Computing (ICSC). California. 06: [3] Ambika P, Samath JA. Visual Query Exploration Proess and Generation of Voluminous Image Database for CBIR Systems. IEEE Conferene Third International Conferene on Computing Communiation & Networking Tehnologies (ICCCNT). Coimbatore. 0: -7. [4] Jyothi B, Latha YM, Reddy VSK. Relvane Feed Bak Content Based Image Retrieval Using Multiple Features. IEEE International Conferene on Computational Intelligene and Computing Researh (ICCIC). Coimbatore. 00: -5. [5] Minarno AE, Kurniawardhani A, Bimantoro F. Image Retrieval Based on Multi Struture Co- Ourene Desriptor. TELKOMNIKA Teleommuniation Computing Eletronis and Control. 06; 4(3): [6] Guizhi Li. Improving Relevane Feedbak in Image Retrieval by Inorporating Unlabelled Images. TELKOMNIKA Indonesian Journal of Eletrial Engineering. 03; (7): [7] Sreedhar J, Raju SV, Babu AV. Query Proessing for Content Based Image Retrieval. International Journal of Soft Computing and Engineering. 0; (5): -3. [8] Patil C, Dalal V. Content Based Image Retrieval Using Combined Features. International Conferene & Workshop on Emerging Trends in Tehnology. New York. 0; : [9] Alnihoud J. Content-Based Image Retrieval System Based on Self Organizing Map, Fuzzy Color Histogram and Substrative Fuzzy Clustering. The International Arab Journal of Information Tehnology. 0; 9(5): [0] Haridas K, Thanamani AS. Well-Organized Content Based Image Retrieval System in RGB Color Histogram, Tamura Texture and Gabor Feature. International Journal of Advaned Researh in Computer and Communiation Engineering. 04; 3(0): [] Sai NST, Patil RC. Image Retrieval Using Bit-Plane Pixel Distribution. International Journal of Computer Siene & Information Tehnology (IJCSIT). 0; 3(3): [] Hussain SJ, Kumar RK. A Novel Approah of Content Based Image Retrieval and Classifiation using Fuzzy Maps. International Journal of Researh Studies in Computer Siene and Engineering. 05; (3): [3] Youness C, Khalid EA, Mohammed O. New Method of Content Based Image Retrieval based on -D ESPRIT Method and the Gabor Filters. Indonesian Journal of Eletrial Engineering and Computer Siene. 05; 5(): [4] Liu Y, Xiao C, Wang Z, Bian C. An One-Class Classifiation Approah to Deteting Porn Image. Conferene on Image and Vision Computing. New York. 0; 7: [5] Karavarsamis S, Pitas I, Ntarmos N. Reognizing Pornographi Images. Multimedia and Seurity. New York. 0; : [6] Ramu B, Thirupathi D. A new methodology for diagnosis of appendiitis using sonographi images in image mining. International Conferene on Computational Siene, Engineering and Information Tehnology. New York. 0; : [7] Kumar A, Nette F, Klein K, Fulham M, Kim J. A Visual Analytis Approah Using the Exploration of Multidimensional Feature Spaes for Content-Based Medial Image Retrieval. IEEE Journal of Biomedial and Health Informatis. 05; 9(5): [8] Ramos J, Kokelkorn TTJP, Ramos I, Ramos R, Grutters J, Viergever MA, Ginneken BV, Campilho A. Content-Based Image Retrieval by Metri Learning From Radiology Report: Appliation to Interstitial Lung Diseases. IEEE Journal of Biomedial and Health Informatis. 06; 0(): 8-9. [9] Haolin Gao. Whih Representation to Choose for Image Cluster. TELKOMNIKA Indonesian Journal of Eletrial Engineering. 04; (8): [0] Kusrini. Klasifikasi Citra dengan Pohon Keputusan. Jurnal Ilmiah Teknologi Informasi (JUTI). 008; 7 (): [] Kusrini, Harjoko, A. Deision Tree as Knowledge Management Tool for Image Classifiation. Knowledge Management International Conferene. Malaysia. 008: [] Kusrini. Differential Diagnosis Knowledge Building by using CUC-C4.5 Framework. Journal of Computer Siene (JCS). 00; 6(): [3] Kusrini K. Grouping of Retail Items by Using K-Means Clustering. Information Systems International Conferene. 05; 3: Multi Features Content-Based Image Retrieval Using Clustering and (Kusrini Kusrini)

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