ISSN: P A Hemalatha et al, International Journal of Computer Science & Communication Networks,Vol 3(3),

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1 Image Retrieval by content using DCT and RGB Projection P.A.Hemalatha M.Tech Advanced Computing, School of Computing, SASTRA University, Tamil Nadu, India Abstract Image retrieval forms a major problem when a large database is considered. Image retrieval by content makes use of the available visual features of the image and helps in retrieving exactly the same image as that of the query image.for this purpose, the proposed work uses three different feature databases from SRM (Statistical Region Merging) algorithm and from DCT (Discrete Cosine Transform). The similarity measurement is given by RGB projection which determines the size of the image and compares the images in the database with the query image. Keywords: CBIR, DCT, RGB projection. 1.Introduction There exist various techniques for image retrieval. Traditional retrieval techniques used textual decriptors to search an image in the database which consumed much time and the users found it to be a difficult process since it required much processing. The other method which overcome the demerits of Keyword search is CBIR which uses the techniques of clustering and feature extraction.the other name for CBIR is Query By Image Content which uses the features in the image for the process of retrieving similar images.some of the features usually found in the images are color, shape and texture.there are various techniques used by CBIR to retrieve images.image retrieval by CBIR is a combination of techniques by both features of low level and high level. This is carried out mainly to diminish the perception level of the user by analyzing the information of the image.the accuracy of the system depends on the methods that are incorporated in feature extraction. The proposed work used three different features databases mainly using shape as the feature. Similarity phase of the work are carried out by evaluating the RGB projection which determines the size of the image horizontally and vertically. The three feature databases are extracted as edge images by using SRM and DCT and the DCT images itself. 2. Related Work [1] Incorporated signature bitstrings for image abstraction mainly to reduce space requirement.global color histogram is used for feature extraction. [2] evaluated descriptors both locally and globally and proposed a new descriptor using Hough transform. [3] proposed an indexing method where feature vector is obtained by color averaging technique and Euclidean distance is used for measurement of similarity. [4] proposed an integrated technique which used wavelet based search and indexing technique therby enabling Multi-class support vector ensemble mainly to support multi class image databases.[5] used a method to retrieve the feature of color by using an integrated technique of K-means and B+ database technique of indexing. [6] contributed a work based on color, edge, color difference and combined a low level features as a whole. [7] proposed a method based on color and texture feature vector extraction based on the co occurrence matrix. [8] used color histogram and Gabor filter for extracting color and texture features respecively. It also used GA (Genetic Algorithm) for feature discrimination. [9] proposed a technique using wavelet based color histogram to retrieve color and texture features from the image. A distance function is incorporated to determine the similarity measure. [10] considered the similarity measure of images by considering various color spaces. [12] performed edge detection mechanism to extract the shape feature by using daubechies and coiflets..[14] made an efficient comntribution by using K means clustering technique and canny edge detection is used to extract the shape feature. [15] used rank power measurement algorithm to search images based on color histogram. [16] focussed on retrieval of images based on IRM (Integrated Region Matching) and color histogram. [17] proposed color and texture features by using color histogram and tammura texture respectively. [18] determined a novel method for shape,color and texture by using color descriptor, pseudo zernike and steerable filter decomposition respectively. 134

2 3. Algorithm The proposed work aimed at retrieving the query image from the images in the database by extracting the feature of shape and simlarity is determined by using RGB projection.the step by step analysis of the proposed work is given below: Step 1: Consider an image in the database as the query image Step 2: Feature databases are considered from three different processing of the query image. Step 3: The first feature database is considered from the border images extracted using SRM (Statistical Region Merging). Step 4: Second feature database is generated by applying DCT on the query image. Step 5: Third feature database is generated by obtaining the edge images from DCT by using Sobel in (BW) Black and white images. Step 6: RGB projection is used to determine the similarity between the query image and the database images. 4. Proposed Work Image retrieval by content retrieves query image from the database by its visual content say shape, color and texture. The proposed work aims at retrieving the images based on its shape. It then makes use of SRM and DCT as main elements in determining the features of shape from the images and is stored in the feature database. Totally there exist three feature databases for processing. Finally by using RGB projection, it determines the similarity between the images. The system architecture is shown in fig 1, Fig1: CBIR system 4.1System requirements A 32 bit windows based system with MATLAB R2010a 4.2 Database: Database consists of collection of images. The proposed work considers 186 leaf images with three different categories as query images for processing. 4.3 Feature extraction: Feature of an image can be shape, color and texture. The proposed work uses shape as a feature to extract from SRM and DCT. Initially it analyzes the image by using SRM where it detects the boundary and stores it as a feature database. Then it applies DCT on the image and stores the DCT images as another feature database.finally it applies sobel on the DCT images thus obtained and stores it as another feature database. Thus the processing takes place in three feature databases Statistical Region Merging (SRM) This algorithm focusses on regions where it segments the given image by merging the similar 135

3 colors together. Here the proposed work uses colormap which supplies false color to the image and determines the average mean color of the image. Also the algorithm generates nine segmention maps since the tuning parameter of segmentation is set from 1 to 256 in the range 1,2,4,8, The scale of segmentation can be adjusted with respect to the tuning parameter. The first feature database is formulated by acquiring the border images along with the precision from SRM algorithm Discrete Cosine Transform DCT forms one of the popular techniques that are used for feature extraction. [11]Other than DCT there are various techniques which are applicable for the same. Traditional techniques used FFT (Fast Fourier Transform). The major drawback of using FFT is that it is not optimal for image coding whereas DCT is shift variant ie, it decomposes the spatial frequency depending on the position of the features in the image. Also it affords high energy compaction.. The proposed work applies DCT on the image to retrieve one of the features which is given by [13], c u, v N 1 N 1 = α u α(v) f x, y cos, x =0 y=0 α u = π 2x + 1 u 2N 1, for u = 0 N 2, for u 0 N cos π 2y + 1 v 2N (1) where, u -denotes regular frequency spatially, v- denotes perpendicular frequency spatially, f(x, y) - is the pixel value at (x, y), C(u, v) -DCT coefficient at (u, v). The steps in applying DCT to an image [14] are as follows: Step 1: Image is partitioned into 8x8 blocks. Step 2: Each block is applied DCT to acquire DCT coefficients. Step 3: On applying DCT repeatedly on DC image, the image size is reduced to 8x8. Step 4: Desired features are then extracted by applying DCT on this block. Step 5: Now the resultant DCT image is stored in the feature database. The major impact of the above said process is that the magnitude changes when there is a shift with the features to a different position. Here the processing involves magnitude value less than Similarity measure Similarity measure does the job of comparing the query image with that of the database images to retreive the similar images in the database. For this purpose, the proposed work uses RGB projection RGB Projection This phase determines the similarity phase of the proposed work where it compares the query image with that of the images in the feature database. RGB projection is a technique which evaluates the image vertically and horizontally. This is mainly to determine the size of the image. This uses the frequency, deviation of the image pixels from the source image. It makes use of Image comparer which determines whether an object is less than, equal to or greater than the other. It also uses a mapping technique where the key is used to sort and uniquely identify the elements and the value stores the content associated with the key. This work also maintains a dictionary frequency which automatically classifies the test images from non target images. The classification is based on the similarities between the target and the non target images. The detailed steps are given below: Step 1: test image is projected onto each one of the target and non target images in the dictionary. Step 2: The target image is now compared with the sum of the images belonging to non target images to determine the similar image. Step 3: Image comparer identifies the duplicate images and identifies the similar images automatically Step 4: It scans the entire collection of database, analyzes its content and locates the similar images. Step 5: returns the image pairs along with their similarity percentage. 136

2, Q1 Q2 Q3 Fig 2: Query images Out of the three query images, one

applied by applying DCT on the original image Fig 6: DCT Image Fig

filtered by Sobel Fig 4 shows the grayscale conversion of the RGB

Fig 7: DCT using Sobel in black and white image Fig 4: Gray image

In the same way all the images in the database are stored as

4 5. Results and Discussion 5.1. Query images Three query images from each category is shown in Fig 2, Q1 Q2 Q3 Fig 2: Query images Out of the three query images, one among them is shown with the steps involved in processing. Fig 3 shows the query image. Fig 5: Border image from SRM Fig 6 shows the DCT image which is applied by applying DCT on the original image Fig 6: DCT Image Fig 3: Original image Fig 7 shows the Black and white images of DCT filtered by Sobel Fig 4 shows the grayscale conversion of the RGB image. Fig 7: DCT using Sobel in black and white image Fig 4: Gray image Fig 5 shows the border extraction acquired from SRM algorithm. In the same way all the images in the database are stored as feature database upon extracting the border. Fig 8 displays the result of similarity by comparing the original image with the feature databse of border images.it also displays the percentage of similarity between the images 137

by comparing black and white DCT images using sobel with query image Fig 10: Percentage of similarity by

5 Fig 8: Percentage of similarity by comparing border images with query image Fig 11: Pixel graph evaluation in DCT images Fig 9: Pixel graph evaluation in border images Fig 12: Percentage of similarity by comparing black and white DCT images using sobel with query image Fig 10: Percentage of similarity by comparing DCT images with query image Fig 13: Pixel graph evaluation in black and white DCT images using Sobel 138

6 Performance Analysis Fig 14 determines the evaluation of precision in percentage among the three query images Fig 14: Precision evaluation in percentage Fig 15 determines the percentage of similarity evaluated against border images, DCT and DCT using sobel Precision in % Fig 15: Similarity evaluation in percentage 6. Conclusion and Futurework The proposed work contributed much towards the accuracy by treating the images in three different feature databases and found the similarity between the images in analogy with the query image indicating the percentage of similarity between the images. This work can be enhanced by using wavelet 76 Q1 Q2 Q Similarity in % Border Images DCT DCT using Sobel based feature extraction for more accuracy of retrieval. 7. References [1] Aly S. Abdelrahim,Mostafa A. Abdelrahaman,Ali Mahmoud,and Aly A. Farag,Image retrieval based on content and image compression,ieee,2011,6696 to [2] Chandan Singh, Pooja Sharma, Performance analysis of various local and global shape descriptors for image retrieval, Multimedia Systems, [3] Dr. H. B. Kekre et al, Color feature extraction for CBIR, International Journal of Engineering Science and Technology (IJEST), Vol. 3 No. 12 December 2011, 8357 to [4]Ela Yildizer, Ali Metin Balci, Tamer N. Jarada, Rebea Alhajj, Integrating wavelets with clustering and Indexing for Effcient content based image retrieval, knowledge based systems 31,2012,55 to 66. [5] Ela Yildizer,Ali Metin Balci, Mohammad Hassan, Reda Alhaj,Effcient content-based image retrieval using Multiple Support Vector Machines Ensemble, Expert Systems with Applications 39, 2012, 2385 to [6] Guang-Hai Liu, Jing-Yu Yang, Content-based image retrieval using color difference histogram, Pattern Recognition 46, 2012, 188 to 198. [7] R. Venkata Ramana Chary, 2Dr. D. Rajya Lakshmi, 3Dr. K. V. N Sunitha, Image Retrieval and Similarity Measurement based on Image Feature, International Journal of Computer Sci ence & Technology,2011,385 to 388. [8]Jun Yue, Zhenbo Li, Lu Liu, Zetian Fu, Content-based image retrieval using color and texture fused features, Mathematical and Computer Modeling 54, 2011, 1121 to [9] M. E. ElAlami, A novel image retrieval model based on the most relevant features, Knowledge-Based Systems 24, 2011, 23 to 32. [10]Manimala Singha and K. Hemachandran, Content Based Image Retrieval using color and texture, Signal Image Processing: An International Journal (SIPIJ) Vol. 3, No. 1, February 2012, 39 to 57. [11] Manimala Singha and Hemachandran, Performance analysis of Color Spaces III Image Retrieval, Assam University. Journal of Science Technology: Physical Sciences and Technology Vol. 7 Number II, 2011, 94 to 104. [12] Mark Nixon, Alberto Aguado, Feature extraction Image processing, [13]Padmashree Desai, Jagadeesh Pujari and Goudar R. H., Image Retrieval using Wavelet based Shape Features, Journal of Information Systems and Communication, Volume 3, Issue 1, 2012,162 to 166. [14] Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, Digital Image Processing using Matlab, [15] Ramamurthy B, Chandran K. R, CBMIR: shape-based image retrieval using canny edge detection and k-means clustering algorithms for medical images, International Journal of Engineering Science and Technology (IJEST), 1870 to 1877,

7 [16] Rishav Chakravarti, Xiannong Meng, A Study of Color Histogram Based Image Retrieval, Sixth International Conference on Information Technology: New Generations, 2009, 1323 to [17] Swati V. Sakhare Vrushali G. Nasre, Design of feature extraction in content based image retrieval (CBIR) using color and texture, International Journal of Computer Science Informatics, Volume-I, Issue-II, 2011, 57 to 61. [18] Xiang-Yang Wang, Yong-Jian Yu,Hong-Ying Yang,An effective image retrieval scheme using color, texture and Shape features, Computer Standards Interfaces 33, 2011, 59 to

International Journal of Computer Engineering and Applications, Volume XII, Issue XII, Dec. 18, ISSN

International Journal of Computer Engineering and Applications, Volume XII, Issue XII, Dec. 18, www.ijcea.com ISSN 2321-3469 A SURVEY ON THE METHODS USED FOR CONTENT BASED IMAGE RETRIEVAL T.Ezhilarasan