Research Article Image Retrieval using Clustering Techniques. K.S.Rangasamy College of Technology,,India. K.S.Rangasamy College of Technology, India.

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1 Journal of Recent Research in Engineering and Technology 3(1), 2016, pp21-28 Article ID J11603 ISSN (Online): , ISSN (Print): Bonfay Publications, 2016 Research Article Image Retrieval using Clustering Techniques K.Karthika 1, R.Nivetha 2, S.Meganathan 3, k.sakthivel 4 1,2,3 UG Scholar, Department of Computer Science and Engineering, K.S.Rangasamy College of Technology,,India. 4Professor, Department of Computer Science and Engineering K.S.Rangasamy College of Technology, India. Received 3 January 2016; Accepted 30 January 2016 Abstract The project deals with graph based image partitioning using spatial data. Traditionally spatial data has been stored and presented in the form of a map. The images are segmented and then the segmented data is transformed by a threshold value given as input. The number of objects or the size is controlled thereby segmentation becomes effective. The algorithm is suitable for both gray scale as well as color images of different types such as bitmap or jpg. The method affords an effective alternative to complex modeling of the original image data while taxing advantage of the computational benefits of graph cuts. The project segments image by keying in any point location inside the automatically starting from the center point. In addition, the statistical data such as number of objects found during segmentation and similar objects with in the image are also calculated. The gray scale conversion of the particular segments is also carried out so that the output image partitions the image into different objects. Suppose the image may be x-ray images which could identify the cell information. Keywords: image portioning, segmentation. 1. INTRODUCTION The first step in the software development life cycle is the identification of the problem. As The first step in the software development life cycle is the identification of the problem. As the success of the system depends largely on how accurately a problem is identified. Although many computer vision algorithms involve cutting a graph (e.g: Normalized cuts), the term graph cuts is applied specifically to those models which employ a max-flow/mincut optimization (other graph cutting algorithms may be considered as graph partitioning algorithms). In graph theory, a cut is a partition of the vertices of a graph of a graph into two disjoint subsets. The cut-set of the cut is the set of edges whose end points are in different subsets of the partition. Edges are said to be crossing the cut if they are in its cut-set. At present, there is a risk in clustering images with more noise pixels. Since the image is not clustered well, the existing 21

2 Journal of Recent Research in Engineering and Technology K. Karthika et al, system is somewhat less efficient. There is no application with this feature to cluster the images with more noise pixels. So, this project identifies that and helps for users to cluster the images through new proposed system with efficient image processing. The software used to solve the problem and develop the application is Microsoft Visual Studio.Net with C# as programming language. The problems are taken in to consideration and try to solve the problem using following modules. 2. RELATED WORK Lin et al. [1] proposed a color-texture and color histogram based image retrieval system (CTCHIR). They proposed (1) three image features, based on color, texture and color distribution, as color co-occurrence matrix (CCM), difference between pixels of scan pattern (DBPSP) and color histogram for K- mean (CHKM) respectively and (2) a method for image retrieval by integrating CCM, DBPSP and CHKM to enhance image detection rate and simplify computation of image retrieval. From the experimental results they found that, their proposed method outperforms the Jhanwar et al. [3] and Hung and Dai [4] methods. Raghupathi et al. [35] have made a comparative study on image retrieval techniques, using different feature extraction methods like color histogram, Gabor Transform, color histogram + gabour transform, Contourlet Transform and color histogram + contourlet transform. Hiremath and Pujari [6] proposed CBIR system based on the color, texture and shape features by partitioning the image into tiles. The features computed on tiles serve as local descriptors of color and texture features. The color and texture analysis are analyzed by using two level grid frameworks and the shape feature is used by using Gradient Vector Flow. The comparison of experimental result of proposed method with other system [7]-[10] found that, their proposed retrieval system gives better performance than the others. Rao et al. [11] proposed CTDCIRS (colortexture and dominant color based image retrieval system), they integrated three features like Motif co occurrence matrix (MCM) and difference between pixels of scan pattern (DBPSP) which describes the texture features and dynamic dominant color (DDC) to extract color feature. The feature extraction method presented in this paper is a combination of the gradient-based feature with wavelet decomposition. In this section, we review the method of texture analysis by gradient-based feature and the theory of wavelet transform. The formula widely adopted for measuring the e/cacyof a CBIR system is also discussed. A. Texture analysis by gradient-based feature Features derived from gradient direction images can be used for texture analysis. Gradient direction images generated by a gradient operator reflect the magnitude and direction of maximal gray-level change at each pixel of an input image. Such information provides important cues for human visual system. A number of gradient operators such as the popular Sobel operator can be used for generating gradient direction images. Assume that there are 360 directions (0 ; 1 ; : : : ; 359 ). By summing up the magnitude value in the same direction at each pixel, a histogram of gradient directions with 360 bins is compiled. Such a histogram can be represented by a vector, called gradient vector, which allows us to analyze the texture of an image in terms of its edginess information. To reduce the length of a gradient vector and the sensitivity due to a small change in image s orientation, every successive k directions can be grouped together to form one bin. Therefore, the total 22

3 K. Karthika et al, Journal of Recent Research in Engineering and Technology number of bins in a histogram of gradient directions will be 360=k. The length of a gradient vector is also 360=k. To measure the difference between two gradient vectors, methods such as Euclidean distance or weighted Euclidean distance can be easily applied. 4. SYSTEM DESIGN The images have been segmented using the different type of image segmentation methods which is described by using the following fig PROPOSED WORK The proposed system is to investigate multi-region graph cut image partitioning via spatial data along with color information. The image data is treated as vertices in the graph and the color differences between the adjacent pixels are treated as edges; during the object identification, if the adjacent vertices are having edge weights greater than the given threshold value, then the two pixels are treated as different objects. The purpose of this project is to segmentation the images using grow cut algorithm with pixel color value differences taken as parameter so that graph cut formulation thereof, becomes applicable. ADVANTAGES The image segmentation is spatially constrained clustering of image data and is effective in segmentation of various types of images. The proposed method shares the advantages of graph cut segmentation via color values optimization. The proposed method brings advantages in regard to segmentation accuracy and flexibility. Statistical data about the number of objects and similarity between objects are possible. Gray scale conversion of interested objects is possible. Median Filter gives the smoothened image output. Fig 1: Different types of Soft thersholding based image segmentation METHOD GRAPH-BASED IMAGE SEGMENTATION OF GRAY SCALE IMAGE The image is segmentated based on the objects present in the image. The pixels are treated as nodes and the difference between the colors in the adjacent nodes are treated as weight of the edges. The region is splitted based on the given weight threshold. GRAPH-BASED IMAGE SEGMENTATION OF RGB IMAGES The module works as the previous module except that the red, green and blue 23

4 Journal of Recent Research in Engineering and Technology K. Karthika et al, components of the pixels are taken into consideration during the threshold value checking for two adjacent pixels. helpful in image classification. If these modules are integrated in some image processing applications, it will be an added feature in the software. GRAY SCALE CONVERSION OF IDENTIFIED OR SELECTED OBJECT(S) To distinguish the objects segmented, they are highlighted with the border of different color. In addition, the objects can be clicked and selected and then converted to gray scale pixels so that the look and feel of the segment image is good. PATTERN RECOGNITION In this module, the whole image is checked with the given pattern image for similarity. The color values are taken into consideration so that up to 90 % color matching pixels are treated as same pattern. 5. EXPERIMENTAL RESULTS Then the proposed image retrieval has been achieved by the different region extraction and pattern recognition method. Then the proposed system is implemented using the MATLAB tool for achieving the better image recognition accuracy. Then the implemented image retrieval process is shown in the following figures. APPLYING MEDIAN FILTER In this module, the noise in the image is filtered by changing the pixel value with median values of surrounding pixels. To apply median filter, for each pixel, the surrounding pixels 3x3 is taken and the gray scale values are summed and median value is found out. The median value is set to the center pixel. This reduces the noise data in segmented image for clear view of output image. STATISTICAL DATA OF OBJECTS SEGMENTED In this module, the statistical information such as the number of objects found out, how many objects are of similar size and shapes nearly matching are also calculated and displayed. The comparison details of these details at various threshold values are also displayed. These will be Fig. 1 Login Form Fig. 2 Segmentation Menu 24

5 K. Karthika et al, Journal of Recent Research in Engineering and Technology Fig. 3 Select Image Menu Fig. 7 Graph based Segmentation Fig. 4 Select Image Form Fig. 8 Patterns Recognized 6. CONCLUSION Fig. 5 Image data saved in the database The project has covered almost the entire request. Further requirements and improvements can be easily structured primarily and modular in nature, because the encoding is performed. Improvements can be appended to by changing the existing modules or adding new modules. Several areas in the future, so that the application will be developed, need to be upgraded for the new necessary and it is possible modifications according to new requirements and specifications. 7. FUTURE WORK Fig 6: Image Segmentation Menu In future, same project will developed in web based application. It should not require software installation. Here the image segmentation only handled, in future plan to 25

6 Journal of Recent Research in Engineering and Technology K. Karthika et al, add the concept of compression and decompression of image which should reduce the image size proficiently. The images are planned to store in the database without affecting real image data. 9.REFERENCES [1]. Content Based Image Retrieval using Color and Texture, Manimala Singha and K.Hemachandran in Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.1, February 2012 DOI. [2]. J. Eakins and M. Graham, Content-Based Image Retrieval, Technical report, JISC Technology Applications Programme, [3]. Y. Rui, T. S. Huang and S.F. Chang, Image Retrieval: Current Techniques, Promising Directions and Open Issues. Journal of Visual Communication and Image Representation. 10(4): pp [4]. A. M. Smeulders, M. Worring and S. Santini, A. Gupta and R. Jain, Content Based Image Retrieval at the End of the Early Years, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12): pp , [5]. Y. Liu, D. Zang, G. Lu and W. Y. Ma, A survey of content-based image retrieval with high-level semantics, Pattern Recognition, Vol- 40, pp , [6]. T. Kato, Database architecture for content-based image retrieval, In Proceedings of the SPIE - TheInternational Society for Optical Engineering, vol.1662, pp , [7]. M. Flickner, H Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafne, D. Lee, D. Petkovic, D. Steele and P. Yanker, Query by Image and Video Content The QBIC System IEEE Computer, pp-23-32, [8]. A. Gupta and R. Jain. Visual information retrieval, Communications of the ACM 40 (5), [9]. A. Pentland, R.W. Picard and S. Scaroff, Photobook: ContentBased Manipulation for Image Databases, International Journal of Computer Vision 18 (3), pp [10]. J. R. Smith and S.F. Chang, VisualSEEk: a fully automated content-based image query system, ACM Multimedia, [11]. J. Wang, G. Wiederhold, O. Firschein and S. We, Content-based Image Indexing and Searching Using Daubechies Wavelets, International Journal on Digital Libraries (IJODL) 1, (4). pp , [12]. C. Carson, S. Belongie, H. Greenspan and J. Malik, Blobworld: image segmentation using expectation-maximization and its application to image querying, IEEE Trans. Pattern Anal. Mach. Intell. 8 (8), pp , [13]. J. Wang, J. LI and G. Wiederhold, SIMPLIcity: Semanticssensitive integrated matching for picture libraries, IEEE Transactions on Pattern Analysis and Machine Intelligence. 23, 9, pp , [14]. C.H. Lin, R.T. Chen and Y.K. Chan, A smart content-based image retrieval system based on color and texture feature, Image and Vision Computing vol.27, pp , [15]. J. Huang and S. K. Ravi, Image Indexing Using Color Correlograms, Proceedings of the IEEE Conference, Computer Vision and Pattern Recognition, Puerto Rico, Jun [16]. G. Pass and R. Zabih, Refinement Histogram for Content-Based Image 26

7 K. Karthika et al, Journal of Recent Research in Engineering and Technology Retrieval, IEEE Workshop on Application of Computer Vision, pp [17]. M. Stricker and A. Dimai, Color indexing with weak spatial constraints, IS&T/SPIE Conf. on Storage and Retrieval for Image and Video Databases IV, Vol. 2670, pp.29-40, [18]. P. S. Suhasini, K. R Krishna and I. V. M. Krishna, CBIR Using Color Histogram Processing, Journal of Theoretical and Applied Information Technology, Vol. 6, No.1, pp , [19]. R. Chakarvarti and X. Meng, A Study of Color Histogram Based Image Retrieval, Sixth International Conference on Information Technology: New Generations, IEEE, Pattern Analysis and Machine Intelligence, Vol. 17, No. 7, [25]. J.R. Smith and S.F. Chang, Automated Image Retrieval using Color and Texture, Technical Report, Columbia University, [26]. V. V. Kumar, N. G. Rao, A. L. N. Rao and V. V. Krishna, IHBM: Integrated Histogram Bin Matching For Similarity Measures of Color Image Retrieval, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 2, No.3, [27]. M. Swain, D. Ballard, Color indexing, International Journal of Computer Vision, 7, pp-11 32, [20]. X. Wan and C.C. Kuo, Color Distrbution Analysis and Quantization for Image Retrieval, In SPIE Storage and Retrieval for Image and Video Databases IV, Vol. SPIE 2670, pp. 9 16, [21]. S. Li and M. C. Lee, Rotation and Scale Invariant Color Image Retrieval Using Fuzzy Clustering, Published in Computer Science Journal, Chinese university of Hong Kong, [22]. F. Tang and H. Tae, Object Tracking with Dynamic Feature Graph, ICCCN 05. [23]. M. Ioka, A Method of defining the similarity of images on the basis of color information, Technical Report IBM Research, Tokyo Research Laboratory, [24]. H. James. H, S. Harpreet, W. Equits, M. Flickner and W. Niblack, Efficient Color Histogram Indexing for Quadratic Form Distance Functions, IEEE Transactions on 27

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