Jan2015 by Editor IJITCE

ISSN (ONLINE) : 2045 -8711 ISSN (ONLINE) : 2045 -8711 ISSN (PRINT) -869X ISSN (PRINT) : 2045 : 2045 -869X

INTERNATIONAL JOURNAL JOURNALOFOFINNOVATIVE INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING JANUARY 2015 FEBRUARY 2015 VOL5 NO 1 VOL- 5 NO - 2

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.1 JANUARY 2015

UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: editor@ijitce.co.uk Phone: +44-773-043-0249 USA: Editor International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA. Phone: 001-706-206-0812 Fax:001-706-542-2626 India: Editor International Journal of Innovative Technology & Creative Engineering Dr. Arthanariee. A. M Finance Tracking Center India 66/2 East mada st, Thiruvanmiyur, Chennai -600041 Mobile: 91-7598208700

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.1 JANUARY 2015

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING Vol.5 No.1 January 2015

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.1 JANUARY 2015

From Editor's Desk Dear Researcher, Greetings! Research article in this issue discusses about motivational factor analysis. Let us review research around the world this month. New Algorithm Should Enable Household Robots to Better Identify Objects research shows that a system using an offthe-shelf algorithm to aggregate different perspectives allows household robots to recognize four times as many objects as one that uses a single perspective, while reducing the number of misidentifications. For household robots ever to be practically need to be able to recognize the objects they are supposed to manipulate. But while object recognition is one of the most widely studied topics in artificial intelligence, even the best object detectors still fail much of the time. Artificial Intelligence Laboratory believe that household robots should take advantage of their mobility and their relatively static environments to make object recognition easier, by imaging objects from multiple perspectives before making judgments about their identity. Matching up the objects depicted in the different images, however, poses its own computational challenges. Shape image retrieval is use of object shape is one of the most challenging problems in creating efficient CBIR. The object's shape plays a critical role in searching for similar image objects. In image retrieval, one expects that the shape description is invariant to scaling, rotation, and translation of the object and is naturally either 2D or 3D depending on the object. Shape features are less developed than their color and texture counterparts because of the inherent complexity of representing shapes. In particular, image regions occupied by an object have to be found in order to describe its shape and a number of known segmentation techniques combine the detection of low-level color and texture features with region-growing or split-and-merge processes. But generally it is hardly possible to precisely segment an image into meaningful regions using low-level features due to the variety of possible projections of a 3D object into 2D shapes, the complexity of each individual object shape, the presence of shadows, occlusions, nonuniform illumination, varying surface reflectivity. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.1 JANUARY 2015

Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering,Universiti Putra Malaysia,UPMSerdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at ShangaiJiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin,Faculty of Agriculture and Horticulture,Asternplatz 2a, D-12203 Berlin,Germany Dr. Marco L. BianchiniPh.D Italian National Research Council; IBAF-CNR,Via Salaria km 29.300, 00015 MonterotondoScalo (RM),Italy Dr. NijadKabbaraPh.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh,Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University,No. 303, University Road,Puli Town, Nantou County 54561,Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources,Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Dr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP. Ph.D. Project Manager - Software,Applied Materials,1a park lane,cranford,UK Dr. Bulent AcmaPh.D Anadolu University, Department of Economics,Unit of Southeastern Anatolia Project(GAP),26470 Eskisehir,TURKEY Dr. SelvanathanArumugamPh.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602,USA.

Review Board Members Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. MutamedTurkiNayefKhatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), TulKarm, PALESTINE.

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Dr. T. Christopher, Ph.D., Assistant Professor &Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India. Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India. Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,RuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRuaItapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 JavadRobati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran VineshSukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE HOD & Associate Professor, IT Dept, Medi-Caps Inst. of Science & Tech.(MIST),Indore, India Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India Dr. doc. Ing. RostislavChoteborský, Ph.D. Katedramateriálu a strojírenskétechnologieTechnickáfakulta,Ceskázemedelskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg.,HamptonUniversity,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India). Dr. Ing. RostislavChotěborský,ph.d, Katedramateriálu a strojírenskétechnologie, Technickáfakulta,Českázemědělskáuniverzita v Praze,Kamýcká 129, Praha 6, 165 21

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.1 JANUARY 2015 Dr. AmalaVijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE Naik Nitin AshokraoB.sc,M.Sc Lecturer in YeshwantMahavidyalayaNanded University Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed. Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-BangloreWesternly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech& PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,MechanicalEngineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ. Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY SeraphinChallyAbou Professor,Mechanical& Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 OrdeanCourt,Duluth, Minnesota 55812-3042 Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America Y. BenalYurtlu Assist. Prof. OndokuzMayis University Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic& Ceramic Materials CSIRO Process Science & Engineering Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. ChutimaBoonthum-Denecke, Ph.D Department of Computer Science,Science& Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.1 JANUARY 2015 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,S찾o Paulo Business School,RuaItapeva, 474 (8째 andar)01332-000, S찾o Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road Suite 100,Virginia Beach, VA 23462 Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India. Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India. Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India PremaSelvarajBsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),UniversitiSainsMalaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, PrannathParnami Institute of Management & Technology, Hissar, Haryana, India RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.1 JANUARY 2015

Contents An Enhanced Technique for Image Retrieval Using Texture Features R.N.Muhammad Ilyas & Dr.S.Pannirselvam ….…………………………………………….……………………….[253]

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.1 JANUARY 2015

An Enhanced Technique for Image Retrieval Using Texture Features R.N.Muhammad Ilyas Ph.D (Research Scholar), Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India. Email: ilyas_rn@yahoo.com Dr.S.Pannirselvam Research Supervisor & Head Department of Computer Science, Erode Arts & Science College (Autonomous), Erode, Tamil Nadu, India. Email: pannirselvam08@gmail.com

Abstract--- Content-Based Image Retrieval (CBIR) uses the visual contents of an image such as color, shape, texture and spatial layout to represent and index the image. The CBIR is the process of retrieving images from a database or library of digital images according to the visual content of the images. Image retrieval is the most essential process in the real world web application where the most of the user attempting to retrieve the images by submitting the label keywords. The image retrieval process is enhanced to improve the retrieval accuracy by retrieving the contents based on visual information present in the images instead of the labelling information. Then feature extraction on image retrieval is to be accomplished. The segmentation is the process of partitioning an image into multiple images. Content based image retrieval is done efficiently by using the combination of the texture and the shape features. Gustafson-kessel algorithm is used for segmentation to improve the retrieval accuracy of the images. The texture features are extracted from the segmented images to calculate the Hausdroff distance for similarity measures. Based on the similarity value, the images in the data bases are retrieved and the performance is evaluated with Corel database of images. The high accuracy, precision and recall are compared with the existing models and are implemented with Mat lab. Keywords-CBIR, TBIR, RBIR, SPCA, CAD, FCM.

I . INTRODUCTION An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning', keywords or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive to address a large amount of research done on automatic image annotation. Text based image retrieval stores the text in the form of keywords together with the image. Some TBIR uses surrounding text of the image to search the keywords which are physically close to the image. Content based image retrieval system makes direct use of content of the image rather than relying on the human annotation of metadata with the keywords. At present the CBIR makes use of low level features like shapes, color and texture to retrieve desired image from database. To obtain efficient image retrieval, tools like pattern recognition and statistics are well used different

implementation of CBIR makes use of different types of queries. Region based image retrieval is an extension of content based image retrieval techniques. Region based image retrieval system provides new query types to search for objects embedded in an arbitrary environment. This system automatically segments images into a variable number of regions and uses a segmentation algorithm to extract a set of features for each region. Context Based Image Retrieval is a comparatively new approach of image retrieval. Context is any information that can be used to characterize the situation of an entity. Context is the where, who, what and when of an object. Texture can define as the visual pattern that has properties of homogeneity not resulting from the presence of only a single color or intensity. Various techniques for texture analysis have been investigated in the field of computer vision and pattern recognition. The texture extraction techniques can be classified into two categories: They are statistical and structural. Statistical approaches use intensity distribution of image to extract statistical parameters representing texture of image. Commonly used statistical methods include Fourier power spectra, Co-occurrence matrices, Shift-invariant principal component analysis (SPCA), Tamura feature, Wold decomposition, Markov random field, Fractal model, and Multi-resolution filtering techniques such as Gabor and wavelet transform. II. LITERATURE SURVEY Nadia[1] developed a retrieving and distributing multimedia data becomes a frequent but still challenging task of retrieving data from large scale multimedia databases with satisfactory accuracy and performance rates. The advent of large scale multimedia databases has led to great challenges in content-based image retrieval (CBIR). In particular, it gives an overview of statistical methodologies and techniques employed for texture feature extraction using most popular spatial-frequency image transforms, namely discrete wavelets, Gabor wavelets, dual-tree complex wavelet and contour lets. It does not achieve high accuracy. Gauri [2] Content Based Image Retrieval (CBIR) the term Content based means that the search will analyze the actual contents (features) of the image. In the image two types of features are present, Low Level Features and High Level Features. It is difficult to extract high level features like emotions or different activities present in that image. But they

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.1 JANUARY 2015 give relatively more information about objects and scenes in the images that are perceived by human beings. The low level features to be used depend upon the applications. It does not consider shape feature for improve accuracy level. Gode [3] described the content-based means that the search analyses the contents of the image other than the metadata. Metadata refers to keywords, tags or descriptions associated with the image. To apply the integration of the above combination, then cluster based on properties create the co-occurrence matrix. Co-occurrence matrix calculates the feature vector for texture. Canny algorithm is use for edge detection to calculate the feature vector for the shape. Invariant moments are then used to record the shape features. Optimal feature extraction is still investigated. Usha [4] developed retrieval of an image is a more effective and efficient for managing extensive image database. In this proposed system, content based image retrieval is accomplished by features. The extracted feature vector of the query image is compared with extracted feature vectors of the database images to obtain the similar images. The main objective this work is classification of image using SVM algorithm. Support vector machine also known as SVM and is a supervised machine learning method that examine the data and identify the patterns, used for classification. The advantage of this algorithm is to classify the input query object depends on feature vectors and training samples. Optimal classification methods are required for improve precision and recall rate. Srinivasa [5] enhanced shape of an object is a binary image representing the extent of the object. This system proposes a method to compute the exact values of the moments by mathematically integrating the Legendre polynomials over the corresponding intervals of the image pixels. Experimental results show that the values obtained match those calculated theoretically and the image reconstructed from these moments have lower error than that of the conventional methods for the same order. Support vector machine is only suitable for smaller data bases it does not suitable for large data base. Mahantesh [6] discussed the content-based image retrieval is the application of computer vision techniques to the image retrieval problem, specifically the search for specific digital images in large databases. The evaluation of the proposed approach is carried out using the standard precision and recall measures. It achieves high retrieving accuracy. Amit [7] revealed the technique which uses visual contents to search images to find a desired image from a collection of databases has wide applications. In this system present an algorithm for retrieving images with respect to a database consisting of engineering/computer-aided design (CAD) models. It does not suitable to investigate whether the proposed shape representation is useful in other application domains, such as protein search in molecular biology. Mahdi [8] developed novel technique for contentbased image retrieval based on tree matching. Image objects and their relations are some of the important features to match similar images. This new algorithm segments the image into some specific regions and then extracts their color, size, position, shape and objectâ&#x20AC;&#x2122;s relation. The proposed algorithm

computes center of each segment and connects center of them to obtain the image graph. By obtaining minimum spanning tree and then tree matching, we compare the image that is being searched in the database against the sample image. The complicated texture image which may rarely cause some fails. III. DRAWBACKS OF EXISTING SYSTEM Content based retrieval is done with the consideration of the texture feature. Various techniques were adapted to retrieve the contents that are stored in the database in the accurate manner. Some of the limitations of the previous researches are given. In the previous research work only texture and color features are considered for calculating the similarity level between query image and the database stored images which might lead to less accurate retrieval of the images. The older version of classification methodologies are used for unifying the similar kind of images which might lead to the less accurate retrieval of contents. Tree matching process used in the existing work might also leads to the more time complexity. IV. METHODOLOGY The existing research methodology of this work is discussed in the detailed manner. In the existing system, content based retrieval is used for retrieve and gives the images for the users as per their requirements. Retrieving Images in the accurate manner might leads to the failure of contents downloads which needs to preserved with the concept. The K-means clustering and Fuzzy C-means clustering are used for segmenting the images. Here the texture and shape features are used for retrieval. 4.1 K-MEANS CLUSTERING K-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. K-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. The problem is computationally difficult (NP-hard) however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data; however, k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes. 4.2 FUZZY C-MEANS CLUSTERING Fuzzy C-means clustering (FCM) is an iterative algorithm that produces optimal partitions based on minimization of the following objective function, Fuzzy cmeans (FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. It is frequently used in pattern recognition.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.1 JANUARY 2015 4.3 PROPOSED METHODOLOGY The present research is considered as two phases. In the first phase, the pre-processing of an image is to be done for a better image retrieval. Then feature extraction on image retrieval is to be accomplished. In the pre-processing stage various filter has been used, among them the spatial noise filter gives better performance for efficient noise removal on image retrieval. Hence, spatial noise filter is applied in the research for pre-processing on image. In the segmentation is the process of partitioning an image into multiple images. Content based image retrieval is done efficiently by using the combination of texture and shape features. Here, Gustafsonkessel algorithm and fuzzy shell clustering are used for segmentation. Then the two algorithms are used to improve the retrieval accuracy of the images. To get a high retrieving accuracy and efficient result instead of k means and fuzzy c means here used Gustafson-kessel algorithm and fuzzy shell clustering for segmentation. The shape and texture features are extracted and combined together for the retrieval. Finally, the features of the query are compared to those of the images in the database in order to rank each image according to its distance to the query. The detailed description of the proposed methodology is discussed. Query images

Segmentation

Feature Extraction

Similarity measurement

Data base images

Display relevant images

4.1 Process Flow 4.3.1 Preprocessing There are numerous tasks to be completed before performing image retrieval. An image must be scanned and converted into gray scale image for pre-processing. Preprocessing consists of a few types of sub process to clean the image and make it appropriate to carry the image retrieval process accurately. Noise can cost the efficiency of the image retrieval system. Noise may occur due the poor quality of the image or that accumulated whilst scanning, but whatever is the cause of its presence it should be removed before further processing. We have used spatial filtering for the removal of the noise from the image. Generally filters are used to filter unwanted things or object in a spatial domain or surface. In digital image processing, mostly the images are affected by various noises. The main objectives of the filters are to improve the quality of image by enhancing is to improve interoperability of the information present in the images for human visual. 4.3.1.1 Spatial Noise Filtering To transferring an image, sometimes transmission problems cause a signal to spike, resulting in one of the three point scalars transmitting a incorrect value. This type of

transmission error is called â&#x20AC;&#x153;salt and pepperâ&#x20AC;? noise due to the bright and dark spots that appear on the image as a result of the noise. The ratio of incorrectly transmitted points to the total number of points is referred to as the noise composition of the image. The goal of a noise removal filter is to take a corrupted image as input and produce an estimation of the original with no foreknowledge of the characteristics of the noise or the noise composition of the image. In images containing noise, there are two challenges. The first challenge is determining noisy points. The second challenge is to determine how to adjust these points. In the Vector Median Filter (VMF), a point in the signal is compared with the points surrounding it as defined by a filter mask. Each point in the mask filter is treated as a vector representing a point in a three dimensional space. Among these points, the summed vector distance from each point to every other point within the filter is computed. The point in the signal with the smallest vector distance amongst those points in the filter is the minimum vector median. The Spatial Median Filter is a new noise removal filter. Spatial Median Filter and the Vector Median Filter follow a similar algorithm and it will be shown that they have comparable results. To improve the quality of the results of the Spatial Median Filter, a new parameter will be introduced and experimental data is shown demonstrating the amount of improvement. The Spatial Median Filter is a uniform smoothing algorithm with the purpose of removing noise and fine points of image data while maintaining edges around larger shapes. 4.3.1.2 Segmentation Segmentation refers to the process of partitioning a digital image into multiple segments. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. The Gustafson-Kessel algorithm associates each cluster with both a point and a matrix, respectively representing the cluster centre and its covariance. Whereas the original fuzzy c-means make the implicit hypothesis that clusters are spherical, the Gustafson-Kessel algorithm is not subject to this constraint and can identify ellipsoidal clusters. The cluster centre is computed as a weighted mean of the data, the weights depending on the considered algorithm, as detailed in the following. This cluster parameter updating step is alternated with the update of the weighting coefficients until a convergence criterion is met. 4.3.4 Gustafson-Kessel Algorithm The Gustafson-Kessel algorithm associates each cluster with both a point and a matrix, respectively representing the cluster centre and its covariance. The original fuzzy c-means make the implicit hypothesis that clusters are spherical, the Gustafson- Kessel algorithm is not subject to this constraint and can identify ellipsoidal clusters. The covariance matrix is defined as a fuzzy equivalent of classic covariance. A size constraint is imposed on the covariance matrix whose determinant must be 1. As a consequence, the GustafsonKessel algorithm can identify ellipsoidal clusters having approximately the same size. This cluster parameter updating step is alternated with the update of the weighting coefficients until a convergence criterion is met.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.1 JANUARY 2015 Algorithm: Gustafson-Kessel Algorithm

Step 1: Step 2: Step 3: Step 4: Step 5: Step 6: Step 7: Step 8:

Input: Query images Output: Segmented regions Start the process Calculate the cluster centre and covariance matrix are computed using fuzzifier. Initialize the cluster. To calculate maximum iteration Calculate the covariance matrices Calculate the distance norms using equation 5.1. Update the coefficients until convergence criteria meet. Repeat the step 3 to step 6.

V. SIMILARITY MEASUREMENT 5.1 HAUSDROFF DISTANCE The Hausdroff distance is defined as function for directed hausdroff distance from A to B. This function discovers the point, which is farthest from any point and calculates the distance from its neighbourhood. --- (5.1) 5.3 ACCURACY Accuracy refers to the degree of conformity and correctness of something when compared to a true or absolute value. The accuracy is calculate by taking the ratio difference between the total number of images present in the database that are use for comparison to the total correct similar images that are retrieved as output. This performance evaluation is conducted in the coral database which has been taken as output value. The accuracy is calculated as follows: --- (5.2) 5.4 PRECISION Precision refers to a state of strict exactness how consistently something is strictly exact. Precision value is determined based on the retrieval of information at true positive prediction, false positive. The precision of image retrieval is calculated based how much percentage the values are retrieved correctly among the total number of images that are retrieved as output. The precision is calculated as follows: Precision =True Positive/ (True Positive + False Positive) ---(5.3) 5.5 RECALL Recall value is determined based on the retrieval of information at true positive prediction, false negative. Recall in this context is also referred to as the True Positive Rate. In that process the fraction of relevant instances that are retrieved. Recall is ratio between the correction predictions of images over the set of images that are taken as input from the values that are retrieved as output. The recall is calculated as follows: Recall =True Positive / (True Positive + False Negative) ---(5.4)

6. EXPERIMENTATION & RESULTS The proposed methodology is experimental with coral data set. The images with different sizes are considered in this data set in the JPEG format. This simulation was conducted in the MATLAB simulation environment which will retrieve the images present in the database in terms of query image which has been submitted. The Content retrieval process is implemented efficiently using the MATLAB toolkit. Coral dataset is considered in this work. This dataset contains image features extracted from a Corel image collection. Four sets of features are available based on the color histogram, color histogram layout, color moments, and co-occurrence. There are 10000 images which contain 100 categories. Every category contains 100 images of size 192Ă&#x2014;128 or 128Ă&#x2014;192 in JPEG format. All images come from Corel Gallery Magic 20, 0000 (8 cds).The first 5000 images form Corel-5K Dataset, and all of the 10000 images form Corel-10K dataset. It only used for academic communication and cannot be used in commercial products. Corel-5K and Corel-10K datasets are used in the algorithms of multi-texton histogram(MTH), micro-structure descriptor(MSD) and color difference histogram(CDH) , which have been accepted for publication in Pattern Recognition. The categories of images that are considered are given in the following figures

Figure 4.2 Sample images from corel 1000 The above figure 4.2 is the various sample images of the image database corel 1000. The images considered for experimentation are from these different classes of their image database. The proposed algorithm Gustafson-kessel based segmentation and the fuzzy shell based segmentation is implemented and the experimentation has been done. A set of training images including deer, lion and tigers are taken and the learning process is done by extracting different features which are then grouped together. For a testing part input image of Lion image is considered as input image for this experimentation for which proposed approach is implemented. The similarity calculating is done with the consideration of the different parameters based on which content based retrieval is done. The input image considered is given in the following figure 4.3.

Figure 4.3 Testing image The above input image is processed in different level for efficient and accurate retrieval of contents that are stored in the database. The processes that are done over the test input image is listed and represented in the following figure 4.4.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.5 NO.1 JANUARY 2015

Figure 4.4 List of operation Initially image would be converted into the gray scale image for the efficient segmentation. By converting into gray scale image accurate segmentation can be done over the given test input image. The grey scale image is represented as follows:

Figure 4.8 Retrieval result 6.2 PERFORMANCE EVALUATION The performance evaluation is conducted in terms different performance metrics which is compared in different levels. The performance measures that are used to compare the effectiveness of the proposed mechanism than the existing approaches are

Figure 4.5 Grey scale image After converting into grey scale image different segmentation mechanism would be applied over the input image extracting the required part alone for efficient construction. The segmented image which was segmented using fuzzy shell clustering algorithm is given as follows

Figure 4.6 Fuzzy shell clustering The segmented image which was segmented using gauston kessel segmentation approach is given as follows:

Figure 4.7 Gauston kessel Based on these segmented result, the comparison would be done with the database stored images based on which image retrieval would be done with the consideration of the various results part. The retrieval result which has been obtained after comparison is given as follows: