Wavelet Transform on Pixel Distribution of Rows & Column of BMP Image for CBIR S. S. Pinge Department of Information Technology, Thadomal Shahani Engineering College, Bandra (West), Mumbai-5. Tel:+9-986998645 s_pinge@yahoo.com R. C. Patil Department of Electronics and Telecommunication Thadomal Shahani Engineering College, Bandra (West), Mumbai-5. Tel:+9-986764252 ravindra_patil2@yahoo.co.in ABSTRACT Retrieving image from large & varied collections using image content(such as color, shape, texture) as a key is challenging & important problem.this paper describes a novel & effective approach to Content Based Image Retrieval (CBIR) that represent each image in database by a vector of feature values called Wavelet Transform on Pixel Distribution of Row & Column of BMP for CBIR. Here we propose a simple and effective approach that can be easily implemented in a programming language. In this technique standard deviation & mean of wavelet coefficients of color distribution of row & column used as a feature vector of image.we use Harr wavelet for this purpose.we obtain compact feature vector of size 24 that create image signature in term of both texture & color. We use simple Euclidean distance to compute the similarity measures of images for Content Based Image Retrieval application. This technique gives acceptable results in a simple and fast way. Categories and Subject Descriptors I.4.7 Image Processing and Computer vision General Terms Algorithms, CBIR, Wavelet Transform,BTC. Keywords Mean standard deviation, wavelet transform, Minkowski distance, precision, recall.. INTRODUCTION People from various fields use image-related information. For instance, doctor s use X-ray image information, cartographers and geographers use aerial image information and meteorologists use satellite image information. These images are almost always converted to digital form and stored in image databases for later use. Most of the image retrieval systems associate keyword or text with each image & are required to enter the keyword or text descriptor of desired image. Through text description, images can be organized by topical or semantic hierarchies to facilitate easy navigation & browsing based on standard Boolean queries. However, since automatically generating descriptive text for a wide spectrum of images is not feasible, most text based image retrieval systems require manual annotation of images. Obviously, annotating images is a cumbersome & expensive task for large image database & is often subjective [0, ].Because of all these problem in recent year, there has been interest in developing effective method for searching large image database on image content by ranking relevance between query image feature vector & database image feature vector.the goal of content based image retrieval is to operate on collection of the images & response to visual queries. CBIR describe the process of retrieving desired image from large collection on the basis of feature that can be automatically extracted from image themselves. This leads to issues such as reducing storage space and querying the database that is, getting the information you need from the database as accurately and quickly as possible. As a consequence of the size of image databases, research on content-based retrieval of images has received much attention recently. According to the scope of representation these features are of two types global feature & local feature.the first category include texture histogram, color histogram, color layout & feature selected by multidimensional discriminates, analysis of a collection of images [][2][4],while color, texture & shape in other category. Contentbased retrieval implies the use of low-level visual features such as shape [6, 7], color [2, 3], and texture [8, 9] for search and retrieval Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. ICAC3 09, January 23 24, 2009, Mumbai, Maharashtra, India. Copyright 2009 ACM 978--60558-35-8 $5.00. Figure.Content Based Image Retrieval System. 347
of images from image databases. Texture, shape and color are the most commonly used low-level features in content based image retrieval. In typical content based image retrieval system(fig.),the visual content of the image in data base form a feature database.to retrieve the image user provide the retrieval system with query image. This system then changes this query image in internal representation of feature vector. The similarities /distance between the feature vector of query image & those of the images in the database are then calculated & retrieval is performed with the aid of an indexing. Features that can be extracted from an image are color, shape & texture.. Color Color is a very important feature in aerial RS image and other single band image. Histogram is the major tool to express color feature.rgb (Red, Green and Blue) color system is usually used to express colorful image..2 Shape Shape is also important image content used in retrieval. The primary mechanisms used for shape retrieval include identification of features such as lines, boundaries, aspect ratio, and circularity and by identifying areas of change or stability via region growing and edge detection..3 Texture Texture is a difficult concept to represent. The identification of specific textures in an image is achieved primarily by modeling texture as two-dimensional grey level 4 variations. The relative brightness of pairs of pixels is computed such that degree of contrast, regularity, coarseness and directionality may be estimated. Performance of image retrieval system can be analyzed by using two parameters..4 Precision Fraction of images retrieved that are relevant Relevant images retrieved / All retrieved images..5 Recall Fraction of relevant images that are retrieved Relevant images retrieved/all relevant images. 2. OUTLINE OF PAPER This paper organized in the following sections: Section II provides the details of color space bit mapping, row pixel & column pixel distribution. Section III introduces new image signature parameters. Section IV shows, testing and results and Section VIII gives Conclusion and further working directions. 3. PROPOSED ALGORITHAM OF CBIR SYSTEM. First resizes the original color image. 2. Split image into R, G &B components. 3. Create a binary bitmaps. 3. To create binary bit map, compare the each pixel with threshold value. Let Xr(i,j),g(i,j),b(i,j) i,2,..m. 3.2 The threshold is computed as mean of each color space m n T r( i, j) m n i j () Original Image (Rose) Red Green Blue Figure.3 Separation of R, G & B component T T 2 m n g( i, j) m n (2) i j b( i, j) 3 m n i j Average of red component is 06.69 Average of green component is 56.744 Average of green component is 36.87 3.3 The binary bitmap is computed as Follows: m n (3) Figure.2 Evaluation of CBIR bm bm if r ( i, j) Τ { if r i j <Τ 0 (, ) if g ( i, j) Τ2 { if g i j <Τ2 2 0 (, ) (4) (5) 348
4. After creation of binary bitmaps compute row pixel distribution & column pixel distribution of each plane. e.g. For given query image row & column pixel distribution for R, G & B plane is shown in table. 5. Generation of wavelet coefficients. 5.. Apply DWT2 Harr Wavelet Transform on row & column pixel distribution obtained in above step. 5.2 It gives us approximation coefficient (RA, GA, BA), horizontal detail Coefficients (RH, GH, BH). 6. Calculate mean of each image for above obtained coefficients to get 2 components. Figure 4.Calculation of row & column pixel For given query image R, G & B Pixel Distributions row and Column Vectors are shown in Fig. 5. Red Plane Green Plane Blue Plane Table. Query image row & column pixel distribution for R, G & B plane bm if b( i, j) Τ3 { if b i j <Τ3 3 0 (, ) (6) Figure.5. Row & Column pixel distribution for R, G & B Components. 7. Further take standard deviation of each image for above obtained coefficients to get total 24 components feature vector. 8. For each image in database & query image 24 components calculated. 9. Minkowski (Euclidean distance when r2) distance is computed between each database image & query image on feature vector to find set of images falling in the class of query image. M / r r d( Q, I) Η Q HI M 0 (7) Q-Query image. I-Database image H Q -Feature vector query image. H I -Feature vector for database image. M-Total no of component in feature vector. 0 Select those images where the distances are less than Preselected value for threshold T.. Selection of threshold by trial & error. Performance of approach can be analyzed using precision & recall. Table 2. Euclidean distance database for Ros00 & Hor00. Distance between Query Image (Ros00) & Database image Distance between Query Image (Hor00) & Database image Image Mean & Std. Deviation Result Image Mean & Std. Deviation Ros00 0 R Ros00 9.9 NR Ros002 33.24 R Ros002 223.2 NR Result 349
Ros003 30.3 R Ros003 66.4 NR Ros004 80.343 R Ros004 64.8 NR Ros005 36.66 R Ros005 63.32 NR Ros006 22.06 R Ros006 74.5 NR Ros007 47.59 R Ros007 25.63 NR Ros008 38.54 R Ros008 244.45 NR Ros009 9.4 R Ros009 223.98 NR Ros00 07.64 R Ros00 208.57 NR Hor00 238.22 NR Hor00 0 R Hor002 27.79 NR Hor002 08.76 R Hor003 64.49 NR Hor003 39.78 R Hor004 94.75 NR Hor004 85.63 R Hor005 27.3 NR Hor005 08.55 R Hor006 76.02 NR Hor006 7.37 R Hor007 202.77 NR Hor007 26.57 R Hor008 203.38 NR Hor008 29.23 R Hor009 83.28 NR Hor009 7.57 R Hor00 9.9 NR Hor00 95.423 R Ele00 287.47 NR Ele00 238.49 NR Ele002 250.99 NR Ele002 64.34 NR Ele003 267.54 NR Ele003 224.83 NR Ele004 220 NR Ele004 205.99 NR Ele005 268.84 NR Ele005 226.73 NR Ele006 276.39 NR Ele006 273.0 NR Ele007 253.84 NR Ele007 29.9 NR Ele008 295.87 NR Ele008 26.46 NR Ele009 230.88 NR Ele009 76.56 NR Ele00 273.5 NR Ele00 249.9 NR Proposed Threshold 48 Rose : 0 Proposed Threshold 48 Horse : 0 350
4. RESULTS A database of 30 images was considered having 3 classes of images. The database has 0 roses images, 0 horse s image. The results are shown in table II for 24 component feature vector. The second column shows results for 24 component feature vector where 2 components are mean of approximate wavelet coefficient & horizontal coefficient for R, G &B planes & 2 components are standard deviation of same coefficient. Table II third column show retrieved images from database for Threshold mentioned in last row in above table 2. For the given Threshold we obtain all the images of same class of query image Ros00 & Hor00.Precision and recall for our CBIR. multi-resolution decomposition[2].this paper introduces a new approach : Wavelet Transform on pixel distribution of Rows & Column of BMP image for improving the accuracy of image retrieval based on content. By using 24 components of mean & standard deviation we see the improvement in the results as precision & recall values are improved in table 3. Further higher moments (forth & fifth) are also considered. The user can specify weights for texture (Y) and color (Cr, Cb) distance components. 6. ACKNOWLEDGMENT We are thankful to Mr. V. A. Bharadi (Lecturer in IT Dept., Thakur college of Engineering, Kandiwali (E)) and Mr. S. R. Aher (HOD ETRX, G P Mumbai) for his constant support. 5. CONCLUSION Many researchers have devoted attention to studying texture using multi-resolution analysis, especially the wavelet transform. The main advantages of the wavelet transform, as a tool for analyzing signals, are () Orthogonality (2) Good spatial and frequency localization, and (3) Ability to perform Table 3. Precision & Recall 7. REFERENCES [] C. Faloutsos, R.Barber, M.Flickner, J.hafner, W. Niblck. D.Petkovic, and W.Equitz, Efficient & Effective Querying by Image Content, J. Intell, Inf. Syst., vol.3, no.3-4, Pp.23-262, 994. [2] A Gupata and R. Jain, Visual Information Retrieval, Comman. ACM, vol.40, no.5, 70-79, 997. [3] J.R.Smith & S.F.Chang, VisualSEEK: A Fully Automated Content Based Query System, in Proc.4 th ACM Intell.Conf.Multimedia, 996, pp 87-98. [4] D.l.Swets and J.Weng, Using Discriminates Eigen- features for Image Retrieval, IEEE Trans.Pattern ANAL.mach.Intell, vol8, no.8, pp 83-837, Aug 999. [5] Sitaram Bhagathy, Kapil Chhabra, A Wavelet- based Image Retrieval System, Vision Research Laboratory Department of Electrical and Computer Engineering 35
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