"Image Retrieval with Image Tile Energy Averaging using Assorted Color Spaces"
(IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No.3, 2011 Image Retrieval with Image Tile Energy Averaging using Assorted Color Spaces Dr. H.B.Kekre1, Sudeep D. Thepade2, Varun Lodha, Pooja Luthra, Ajoy Joseph, Chitrangada Nemani 3 1 Senior Professor, 2Ph.D.Research Scholar & Associate Professor, 3B.Tech (IT) Student Information Technology Department, MPSTME, SVKM‟s NMIMS (Deemed-to-be University), Mumbai, India 1 firstname.lastname@example.org, email@example.com,firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com Abstract— Here the feature vector for image retrieval is composed of average energy of each tile of image for diverse number of image A Content Based Image Retrieval (CBIR) is an interface tiles (like 1, 4, 9, 16, 25, 36 and 49) considered with the help of between a high level system (the human brain) and a low level various color spaces. The paper presents exhaustive performance system (a computer). The human brain is capable of performing comparison of 70 variants of proposed image retrieval technique complex visual perception, but is limited in speed while a using ten sundry color spaces and seven image tiling methods is done with the help of generic image database having 1000 images computer is capable of limited visual capabilities at much higher spread across 11 categories. For each proposed CBIR technique 55 speeds. In a CBIR, features are used to represent the image queries (randomly selected 5 per category) are fired on the generic content. The features are extracted automatically and there is no image database. To compare the performance of image retrieval manual intervention, thus eliminating the dependency on humans techniques, average precision and recall are computed and plotted in the feature extraction stage. These automated approaches to against number of retrieved images. The results have shown that object recognition are computationally expensive, difficult and RGB and HSI color spaces give the best performance for average tend to be domain specific. The typical CBIR system performs energy based image retrieval across all tiles. Also it has been seen in two major tasks [16,17]. The first one is feature extraction (FE), all luminance-chromaticity based color spaces ( Kekre’s LUV, where a set of features, called feature vector, is generated to YCbCr, YUV, YIQ and Kekre’s YCgCb) that as the number tiles increased the overall performance also increases. accurately represent the content of each image in the database. The second task is similarity measurement (SM), where a Keywords: CBIR, Average Energy, Color Spaces, Image Tiling. distance between the query image and each image in the database using their feature vectors is used to retrieve the top I. INTRODUCTION “closest” images [16,17,26]. For feature extraction in CBIR there The large numbers of images which are being generated from a are mainly two approaches  feature extraction in spatial variety of sources (digital camera, digital video, scanner, the domain and feature extraction in transform domain. The feature internet etc.) have posed technical challenges for computer extraction in spatial domain includes the CBIR techniques based systems to store/transmit and index/manage image data on histograms , BTC [1,2,16], VQ [21,25,26]. The transform effectively to make such collections easily accessible. Image domain methods are widely used in image compression, as they compression deals with the challenge of storage and give high energy compaction in transformed image [17,24]. So it transmission, where significant advancements have been made is obvious to use images in transformed domain for feature [1,4,5]. The challenge to image indexing is studied in the context extraction in CBIR . But taking transform of image is time of image database [2,6,7,10,11], which has become one of the consuming and also needs all images of database to be of same promising and important research area for researchers from a size to get similar feature vectors. This limitation is overcome wide range of disciplines like computer vision, image processing here in proposed CBIR methods using average energy concept and database areas. The thirst for better and faster image with help of image tiling. retrieval techniques is increasing day by day. Problems with traditional methods of image indexing have led to the rise in techniques for retrieving images on the basis of automatically II. CONSIDERED COLOR SPACES derived features such as color, texture and shape- a technology Including RGB color space, in all ten assorted color spaces are now referred as Content-Based Image Retrieval (CBIR). Some considered here. of the important applications for CBIR technology could be identified as art galleries [12,14], museums, archaeology , A. Kekre’s LUV Color Space architecture design [8,13], geographic information systems , Kekre‟s LUV color Space is special case of Kekre Transform. weather forecast [5,22], medical imaging [5,18], trademark Where L gives luminance and U and V gives chromaticity values databases [21,23], criminal investigations [24,25], image search of color image. Positive value of U indicates prominence of red on the Internet [9,19,20]. component in color image and negative value of V indicates prominence of green component. This needs the conversion of 280 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No.3, 2011 RGB to LUV components. The RGB to LUV conversion matrix The YUV to RGB conversion matrix given in equation (6) gives given in equation 1 gives the L, U, V components of color image the R, G, B components of color image for respective Y, U, V for respective R, G, B components. components. L 1 1 1 R R 0.7492 0.50901 1.1398 Y U 2 1 1. G (1) G 1.0836 0.22472 0.5876 .U (6) V 0 1 1 B B 0.97086 1.9729 0.000015 V The LUV to RGB conversion matrix given in equation 2 gives D. YIQ Color Space the R, G, B components of color image for respective L, U, V The YIQ color space is derived from YUV color space and is components. optionally used by the NTSC composite color video standard. The ‟I‟ stands for in phase and „Q‟ for quadrature, which is the R 1 2 0 L/3 modulation method used to transmit the color information. G 1 1 1.U / 6 (2) Y 0.299 0.587 0.144 R B 1 1 1 V /2 I 0.595716 0.274453 0.321263 . G (7) Q 0.211456 0.522591 0.31135 B B. YCbCr Color Space In YCbCr color Space, Y gives luminance and Cb and Cr gives The inter-conversion equations for YIQ to RGB color space are chromaticity values of color image. To get YCbCr components given as per the equations (7) and (8). we need the conversion of RGB to YCbCr components. The R 1 0.9563 0.6210 Y RGB to YCbCr conversion matrix given in equation 3 gives the G 1 0.2721 0.6474 . I (8) Y, Cb, Cr components of color image for respective R, G, and B B 1 1.107 1.7046 Q components. Y 0.2989 0.5866 0.1145 R E. Kekre’s YCgCb Color Space Cb 0.1688 0.3312 0.5000 . G (3) Inter-conversion equations for RGB to Kekre‟s YCgCb color Cr 0.5000 0.4184 0.0816 B space can be given as below in equations 9 and 10. The YCbCr to RGB conversion matrix given in equation 4 gives Y 1 1 1 R the R, G, B components of color image for respective Y, Cb, and Cg 1 1 0 .G (9) Cr components. R 1 0.0010 1.4020 Y Cb 1 0 1 B G 1 0.3441 0.7140 . Cb (4) R 1 1 1 Y /3 B 1 1.7718 0.0010 Cr G 1 1 0 . Cg / 2 (10) B 1 0 1 Cb / 2 C. YUV Color Space The YUV model defines a color space in terms of one luminance F. XYZ Color Space (brightness) and two chrominance (color) components. The YUV Conversion equations for RGB to XYZ color space and XYZ to color model is used in the PAL, NTSC, and SECAM composite RGB can be given as given in equations 11 and 12 below. color video standards. Previous black-and-white systems used X 0.412453 0.357580 0.180423 R only luminance (Y) information and color information (U and V) Y 0.212671 0.71160 0.072169 . G was added so that a black-and-white receiver would still be able (11) to display a color picture as a normal black and white pictures. Z 0.019334 0.119193 0.950227 B YUV models human perception of color in a different way than the standard RGB model used in computer graphics hardware. R 3.240479 1.537150 0.498535 X The human eye has fairly little color sensitivity: the accuracy of the brightness information of the luminance channel has far more G 0.969256 1.875992 0.041556 . Y (12) impact on the image discerned than that of the other two. The B 0.055648 0.204043 1.057311 Z RGB to YUV conversion matrix given in equation 5 gives the Y, U, V components of color image for respective R, G, B components. G. rgb Color Space (Normalized RGB) Y 0.299 0.587 0.144 R In order to eliminate the influence of illumination intensity, color information (R, G and B) can be normalized to get rgb color U 0.14713 0.22472 0.436 . G (5) space where, V 0.615 0.51498 0.10001 B (13) 281 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, 2011 I. HSI Color Space H. HSV Color Space To convert RGB to HSI [29,30], first we convert RGB to The HSV stands for the Hue, Saturation and Value based on the „normalized rgb‟ using equations given in section 2.2.G. Each artists (Tint, Shade, and Tone). The Value represents intensity normalized H, S and I are then obtained using following of a color, which is decoupled from the color information in the equations. represented image. The Hue and Saturation components are 1 r g r b h cos 1 intimately related to the way human eye perceives color resulting , h 0, , forb g 2 in image processing algorithms with physiological basis. 1 (20) 2 r g g b 2 Conversion formula from RGB to HSV is as follows. r g (14) 1 r g r b h 2 cos 1 2 , h ,2 , for, b g (21) 1 2 r g r g g b 2 (15) s 1 3. minr, g , b , s 0,1 (22) (16) i ( R G B) /(3.255) , i 0,1 (23) Conversion from HSV space to RGB space is more complex. For convenience h,s and i values are converted in the ranges of And, given to the nature of the hue information, we will have a [0,360],[0,100] and [0,255] respectively using following different formula for each sector of the color triangle. equation 24. H h 180 / , S s 100, I i 255 (24) Red-Green Sector: for (17) III. IMAGE TILING  Tiling of an image is basically dividing an image into different equal sized, non overlapping quadrants for feature extraction. Here seven assorted image tiling techniques are applied on images for feature extraction per colour space in proposed CBIR methods. Green-Blue Sector: for (18) IV. PROPOSED CBIR TECHNIQUES Energy averaging could be defined as saverage of squared values of pixels of the respective image tile. Here average energy technique is applied along with tiling for feature vector generation. Feature vector size for respective image tiling Blue-Red Sector: method is shown in table 1. for (19) 1-Tile 4-Tile 9-Tile 16-Tile 25-Tile 36-Tile 49-Tile Image Tiling Method (1x1) (2x2) (3x3) (4x4) (5x5) (6x6) (7x7) Number of Tiles 1 4 9 16 25 36 49 Feature vector Size 3 12 27 48 75 108 147 Table 1 Feature vector size for respective image tiling method considered 282 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No.3, 2011 V. IMPLEMENTATION definitions for these two measures are given by following The implementation of the CBIR techniques is done in equations. MATLAB 7.0 using a computer with Intel Core 2 Duo Number _ of _ relevant_ images_ retrieved Processor T8100 (2.1GHz) and 2 GB RAM. The CBIR Pr ecision Total _ number_ of _ images_ retrieved (25) techniques are tested on the image database  of 1000 variable size images spread across 11 categories of human being, animals, natural scenery and manmade things. The Number _ of _ relevant_ images_ retrieved Re call categories and distribution of the images are Tribes (85), Total _ number_ of _ relevent _ images_ in _ database (26) Buses (99), Beaches (99), Dinosaur (99), Elephants (99), Roses (99), Horses (99), Mountains (61), Airplanes (100), Monuments (99) and Sunrise (61). Figure 2 gives the sample images from generic image database. VI. RESULTS AND DISCUSSION For testing the performance of each proposed CBIR technique randomly selected five images from each category are fired on the database as queries. The average precision and average recall are computed by grouping the number of retrieved images. Figure 2 gives performance comparison of assorted color spaces with average energy technique based on image tiling. It is seen that as the image tiling increases so does the performance increase. The overall best performance is given by HSI color space in tile-9 (3x3). As far as the color spaces go, the best results for each of the tiling methods is given by RGB and HIS color spaces. Also it has been noted that as the no of tiles goes on increasing in all luminance-chromaticity Figure 1: Sample images of Generic Image Database [Image based color spaces like Kekre‟s LUV, YCbCr, YUV, YIQ database contains total 1000 images with 11 categories] and Kekre‟s YCgCb, the overall performance also To assess the retrieval effectiveness, we have used the increases precision and recall as statistical comparison parameters [1,2] for the proposed CBIR techniques. The standard 1X1 2X2 3X3 4X4 5X5 6X6 7X7 0.42 Crossover Point Value of Average Precision 0.41 0.4 0.39 0.38 0.37 0.36 0.35 and Average Recall 0.34 0.33 0.32 0.31 0.3 0.29 0.28 0.27 0.26 0.25 0.24 0.23 RGB HSI XYZ HSV rgb YCgCb LUV YUV YIQ YCbCr Assorted Color Spaces Considered for Amendment of Energy Average Figure 2: Comparison of considered image tiling methods in proposed CBIR techniques for individual color space consideration 283 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No.3, 2011 RGB HSI XYZ HSV rgb YCgCb LUV YUV YIQ YCbCr 0.42 Crossover Point Value of Average Precision and 0.41 0.4 0.39 0.38 0.37 0.36 0.35 Average Recall 0.34 0.33 0.32 0.31 0.3 0.29 0.28 0.27 0.26 0.25 0.24 0.23 1X1 2X2 3X3 4X4 5X5 6X6 7X7 Assorted Color Spaces Considered for Amendment of Energy Average Figure 3: Comparison of color spaces considered in proposed CBIR methods for individual tiling consideration Figure 3 shows the performance comparison of image tiling VIII. 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Thepade, “Image Retrieval using Panoramic View using Medley of Grayscale and Color Partial Color-Texture Features Extracted from Walshlet Pyramid”, Images ”, WASET International Journal of Electrical, ICGST International Journal on Graphics, Vision and Image Computer and System Engineering (IJECSE), Volume 2, No. Processing (GVIP), Volume 10, Issue I, Feb.2010, pp.9-18, 3, Summer 2008. Available online at Available online www.waset.org/ijecse/v2/v2-3-26.pdf. www.icgst.com/gvip/Volume10/Issue1/P1150938876.html  Stian Edvardsen, “Classification of Images using color, CBIR  H.B.Kekre, Sudeep D. Thepade, “Color Based Image Distance Measures and Genetic Programming”, Ph.D. Thesis, Retrieval using Amendment Block Truncation Coding with Master of science in Informatics, Norwegian university of YCbCr Color Space”, International Journal on Imaging (IJI), science and Technology, Department of computer and Info. Volume 2, Number A09, Autumn 2009, pp. 2-14. Available science, June 2006. online at www.ceser.res.in/iji.html (ISSN: 0974-0627).  H.B.Kekre, Tanuja Sarode, Sudeep D. Thepade, “DCT  H.B.Kekre, Tanuja Sarode, Sudeep D. Thepade, “Color- Applied to Row Mean and Column Vectors in Fingerprint Texture Feature based Image Retrieval using DCT applied on Identification”, In Proceedings of International Conference on Kekre’s Median Codebook”, International Journal on Computer Networks and Security (ICCNS), 27-28 Sept. 2008, Imaging (IJI), Volume 2, Number A09, Autumn 2009,pp. 55- VIT, Pune. 65. Available online at www.ceser.res.in/iji.html (ISSN:  Zhibin Pan, Kotani K., Ohmi T., “Enhanced fast encoding 0974-0627). method for vector quantization by finding an optimally-  H.B.Kekre, Sudeep D. Thepade, Akshay Maloo “Performance ordered Walsh transform kernel”, ICIP 2005, IEEE Comparison for Face Recognition using PCA, DCT International Conference, Volume 1, pp I - 573-6, Sept. 2005. &WalshTransform of Row Mean and Column Mean”, ICGST  H.B.kekre, Sudeep D. Thepade, “Improving „Color to Gray International Journal on Graphics, Vision and Image and Back ‟ using Kekre’s LUV Color Space”, IEEE Processing (GVIP), Volume 10, Issue II, Jun.2010, pp.9-18, International Advanced Computing Conference 2009 Available online (IACC’09), Thapar University, Patiala, INDIA, 6-7 Mar http://22.214.171.124/gvip/Volume10/Issue2/P1181012028.p 2009. Available at IEEE Xplore. df..  H.B.Kekre, Sudeep D. Thepade, “Image Blending in Vista  H.B.Kekre, Sudeep D. Thepade, “Improving the Performance Creation using Kekre's LUV Color Space”, SPIT-IEEE of Image Retrieval using Partial Coefficients of Transformed Colloquium and International Conference, Sardar Patel Image”, International Journal of Information Retrieval, Serials Institute of Technology, Andheri, Mumbai, 04-05 Feb 2008. Publications, Volume 2, Issue 1, 2009, pp. 72-79 (ISSN:  H.B.Kekre, Sudeep D. Thepade, “Color Traits Transfer to 0974-6285) Grayscale Images”, In Proc.of IEEE First International  H.B.Kekre, Sudeep D. Thepade, Archana Athawale, Anant Conference on Emerging Trends in Engg. & Technology, Shah, Prathmesh Verlekar, Suraj Shirke, “Performance (ICETET-08), G.H.Raisoni COE, Nagpur, INDIA. Uploaded Evaluation of Image Retrieval using Energy Compaction and on online IEEE Xplore. Image Tiling over DCT Row Mean and DCT Column Mean”,  http://wang.ist.psu.edu/docs/related/Image.orig (Last referred Springer-International Conference on Contours of Computing on 23 Sept 2008) Technology (Thinkquest-2010), Babasaheb Gawde Institute  H.B.Kekre, Sudeep D. Thepade, “Using YUV Color Space to of Technology, Mumbai, 13-14 March 2010, The paper will Hoist the Performance of Block Truncation Coding for Image be uploaded on online Springerlink. Retrieval”, IEEE International Advanced Computing  H.B.Kekre, Tanuja K. Sarode, Sudeep D. Thepade, Vaishali Conference 2009 (IACC09), Thapar University, Patiala, Suryavanshi,“Improved Texture Feature Based Image INDIA, 6-7 Mar 2009. Retrieval using Kekres Fast Codebook Generation  H.B.Kekre, Sudeep D. Thepade, Archana Athawale, Anant Algorithm”, Springer-International Conference on Contours Shah, Prathmesh Verlekar, Suraj Shirke,“Energy Compaction of Computing Technology (Thinkquest-2010), Babasaheb and Image Splitting for Image Retrieval using Kekre Gawde Institute of Technology, Mumbai, 13-14 March 2010. Transform over Row and Column Feature Vectors”, 285 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No.3, 2011  H.B.Kekre, Tanuja K. Sarode, Sudeep D. Thepade, “Image „Manshodhan 2010‟ and Best Paper Award at Springer Int. Conf. Retrieval by Kekre’s Transform Applied on Each Row of ICCCT-2010. Walsh Transformed VQ Codebook”, (Invited), ACM- Ajoy Joseph, Chitrangada Nemani, Pooja Luthra, Varun Lodha International Conference and Workshop on Emerging Trends are currently pursuing B.Tech. (IT) from MPSTME, SVKM‟s in Technology (ICWET 2010),Thakur College of Engg. And NMIMS University, Mumbai. There area of interest is Content Based Tech., Mumbai, 26-27 Feb 2010, Invited paper at ICWET Image Retrieval in Image Processing 2010 available at online ACM Portal.  H.B.Kekre, Sudeep D. Thepade, Adib Parkar, “A Comparison of Kekres Fast Search and Exhaustive Search for various Grid Sizes used for Colouring a Greyscale Image”, 2nd International Conference on Signal Acquisition and Processing (ICSAP 2010), IACSIT, Bangalore, pp. 53-57, 9- 10 Feb 2010, The paper is uploaded on online IEEE Xplore. IX. AUTHOR BIOGRAPHIES Dr. H. B. Kekre has received B.E. (Hons.) in Telecomm. Engineering. from Jabalpur University in 1958, M.Tech (Industrial Electronics) from IIT Bombay in 1960, M.S.Engg. (Electrical Engg.) from University of Ottawa in 1965 and Ph.D. (System Identification) from IIT Bombay in 1970 He has worked as Faculty of Electrical Engg. and then HOD Computer Science and Engg. at IIT Bombay. For 13 years he was working as a professor and head in the Department of Computer Engg. at Thadomal Shahani Engineering. College, Mumbai. Now he is Senior Professor at MPSTME, SVKM‟s NMIMS University. He has guided 17 Ph.Ds, more than 100 M.E./M.Tech and several B.E./B.Tech projects. His areas of interest are Digital Signal processing, Image Processing and Computer Networking. He has more than 320 papers in National / International Conferences and Journals to his credit. He was Senior Member of IEEE. Presently He is Fellow of IETE and Life Member of ISTE Recently ten students working under his guidance have received best paper awards and two have been conferred Ph.D. degree of SVKM‟s NMIMS University. Currently 10 research scholars are pursuing Ph.D. program under his guidance. Sudeep D. Thepade has Received B.E.(Computer) degree from North Maharashtra University with Distinction in 2003. M.E. in Computer Engineering from University of Mumbai in 2008 with Distinction, currently pursuing Ph.D. from SVKM‟s NMIMS University, Mumbai. He has about than 08 years of experience in teaching and industry. He was Lecturer in Dept. of Information Technology at Thadomal Shahani Engineering College, Bandra(w), Mumbai for nearly 04 years. Currently working as Associate Professor in Computer Engineering at Mukesh Patel School of Technology Management and Engineering, SVKM‟s NMIMS University, Vile Parle(w), Mumbai, INDIA. He is member of International Association of Engineers (IAENG) and International Association of Computer Science and Information Technology (IACSIT), Singapore. He is reviewer for many international journals and in the international advisory panel for many international conferences. He has worked as member of International Advisory Committee for many International Conferences. His areas of interest are Image Processing and Computer Networks. He has about 100 papers in National/International Conferences/Journals to his credit with a Best Paper Award at Int. Conference SSPCCIN-2008, Second Best Paper Award at ThinkQuest-2009 National Level faculty paper presentation competition, second award for research project at 286 http://sites.google.com/site/ijcsis/ ISSN 1947-5500