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My2421322135

  1. 1. A. Jwalitha, M.V.Srikanth / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.2132-2135 Blurred And Compressed Trademark Image Retreival Under Noise And Orientation Based On Curvature Shape Descriptor (Csd) A. Jwalitha1 , M.V.Srikanth2 1 Student II M.TECH 2 Assistant Professor, Department of ECE Gudlavalleru Engineering College, Krishna (Dt), India, A.P.Abstract Digital images are a convenient media for distance is shown to handle both complete and partialdescribing and storing spatial, temporal, spectral, multi-textured queries. But, there are severaland physical components of information contained problems with these methods. First of all, humanin a variety of domains (e.g.. aerial/satellite images intervention is required to describe and tag thein remote sensing, medical images in telemedicine, contents of the images in terms of a selected set offingerprints in forensics, museum collections in captions and keywords. In most of the images thereart history, and registration of trademarks and are several objects that could be referenced, eachlogos). Retrieval of digital images is one of the having its own set of attributes. Further, we need tochallenging issue in any Digital Image Processing express the spatial relationships among the varioussystem. Although advances in image compression objects in an image to understand its content. As thealgorithms have alleviated the storage size of the image databases grow, the use ofrequirement to some extent, the large volume of keywords becomes not only complex but alsothese images makes it difficult for a user to browse inadequate to represent the image content. Thethrough the entire database. Therefore, an keywords are inherently subjective and not unique.efficient and automatic procedure based on Often, the preselected keywords in a givencurvature shape descriptors is proposed, which application are context dependent and do not allowmake use shape descriptor along with for any unanticipated search. If the image database iscompression techniques to make database feasible to be shared globally then the linguistic barriers willto store large number of images and retrieve make the use of keywords ineffective.image under noise, blurrring and orientation Color is one of the most widely used low-changes. In CSD approach, the image is subjected level features in the context of indexing and retrievalto compression and then later it is represented in based on image content [7]. It is relatively robust toits contour format by its coordinates and are background complication and independent of imagemathematically processed for curvature evolution size and orientation. Typically, the color of an imageover various sigma levels so as to remove the is represented through some color model. Oneunevenness caused by some external disturbances. representation of color content of the image is byFor each sigma level, zero crossing points are using color histogram. For image retrieval, histogramevaluated which are used as the features for image of query image is then matched against histogram ofretrieval along with arc length. all images in the database using some similarity metric. However, color histograms do not incorporateKeywords: Arc length, Blurring, Compression spatial adjacency of pixels in the image and may leadtechnique, Curvature Shape Descriptor (CSD), to inaccuracies in the retrieval.Noise, Orientation changes, Zero crossing points Although color can be an effective means of querying, color alone as a retrieval cue cannot be1. Introduction effective for querying large image databases. Colour, texture and shape information have Applications with grayscale or binary images have tobeen the primitive image descriptors in content based use other cues such as shape and texture for retrieval.image retrieval systems[3]. Traditionally, textual Although, humans can effectively use color tofeatures, such as filenames, caption and keywords differentiate among natural objects, many artificialhave been used to annotate and retrieve images. (manmade) objects cannot be distinguished on theNonlinear modified discrete Radon transform [5] has basis of color alone. Moreover, humans whenbeen applied to estimate visual contents of textural presented with binary or grayscale images can easilyinformation of an image, such as orientation, distinguish among these.directionality, and regularity. Images of either Image description consists in one of the keyindividual or multiple textures are best described by elements of multimedia information description. Indistributions of spatial frequency descriptors, rather the Multimedia Content Description Interfacethan single descriptor vectors over presegmented (MPEG-7) images are described by their contentsregions. A retrieval method based on the Earth featured by color, texture and shape. The shapeMovers Distance [6] with an appropriate ground descriptor aims to measure geometric attributes of an 2132 | P a g e
  2. 2. A. Jwalitha, M.V.Srikanth / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.2132-2135object to be used for classifying, matching, and important frequencies are discarded throughrecognizing objects.There are several techniques quantization and important frequencies are used toavailable for shape representation as Fourier retrieve the image during decompression.descriptors, Wavelet descriptors, grid-based, The rest of paper is organized as follows. InDelaunay triangulation, among others. Shape section 2, we explain the algorithm for proposeddescription techniques are classified into boundary method. In section 3, the results of the proposedbased and region based methods. Boundary based system are shown and its performance is analysed.methods use only the contour of the objects’ shape, Section 4 gives concluding remarkswhile the region based methods use the internaldetails in addition to the contour. The specified Shape 2.NOVEL APPROACH FOR IMAGErepresentation methods proposed fail to satisfy one or RETRIEVINGmore of such as criteria Invariance, Uniqueness, This section describes novel approach forStability, Efficiency, Ease of implementation, and image retrieving using Curvature Shape Descriptor.Computation of shape properties. In this approach the trained images are subjected to compression and then later it is represented in its The classical Curvature Scale Space method contour format by its coordinates and isfor contour representation captures describes and mathematically processed for curvature evolutioncompares characteristic shape features of over various sigma levels so as to remove theobjects[1][2][4]. It represents two dimensional shapes unevenness caused by some external disturbances.at different resolutions. Maxima (peaks) of CSS For each sigma level, zero crossing points areimages are used to describe the shape and to perform evaluated which are used as the features for imagematching between two curves under analysis. The retrieval along with arc length. Later the retrievalmatching scheme is based on the Euclidean distance module receives user query, applies CSD approach tobetween the peaks of the CSS images. This scheme is obtain image features arc length and zero crossingvery complex and expensive points .The retrieval module is based on Euclidean distance that compares query image features with High-resolution images can occupy large trained images in the database. The designamounts of storage (around 17.5 Mb for one A5 color architecture for proposed approach is specified belowimage scanned at 600 dpi). The need to compressimage data for machine processing, storage andtransmission was therefore recognized early on. Toovercome these effects specified, CSD approach hasbeen proposed, which is an efficient and automaticprocedure is required for indexing and retrievingimages from databases. In this approach the image issubjected to compression and then later it isrepresented in its contour format by its coordinatesand is mathematically processed for curvatureevolution over various sigma levels so as to removethe unevenness caused by some externaldisturbances. For each sigma level, zero crossingpoints are evaluated which are used as the featuresfor image retrieval along with arc length. Data compression is the technique to reducethe redundancies in data representation in order todecrease data storage requirements and hencecommunication costs [8][9][10]. Reducing the Fig 2.1 system architecturestorage requirement is equivalent to increasing the The designed system architecture is ascapacity of the storage medium and hence presented in figure 2.1 The algorithm for proposedcommunication bandwidth. Thus the development of approach is specified below.efficient compression techniques will continue to be a 2.1 DCT BASED COMPRESSION High-resolutiondesign challenge for future communication systems images can occupy large amounts of storage (aroundand advanced multimedia applications. A technique 17.5 Mb for one A5 color image scanned at 600 dpi).to Image compression is the application of Data The need to compress image data for machinecompression on digital images. The discrete cosine processing, storage and transmission was thereforetransform (DCT) is a technique for converting a will continue to be a design challenge for futuresignal into elementary frequency components. It is communication systems and advanced multimediawidely used in image compression. DCT separates applications. A technique to Image compression isimages into parts of different frequencies where less 2133 | P a g e
  3. 3. A. Jwalitha, M.V.Srikanth / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.2132-2135the application of Data compression on digital P and Q are the first order derivative of p, q and P,images. The discrete cosine transform (DCT) is a Q’’ are the second order derivatives.technique widely used in image compression. C (u, σ) is smoothened curvature.[10]DCT separates images into parts of different For the obtained smoothened curvature at eachfrequencies where less important frequencies are gaussian level, zero crossings are computed.discarded through quantization and importantfrequencies are used to retrieve the image during 2.6 ZERO CROSSING COMPUTATIONdecompression. Typically, input taken images are After smoothening the given curvature acompressed in macroblocks of 8 rows by 8 columns, zero cross is evaluated, where the zero cross is foundwhere each block is linearized into a one-dimensional when the tracing come across a pixel variation from 0vector. Various level of compression can be achieved to 1 or 1 to 0 level.using this algorithm. The compressed image is passedto preprocessing unit (canny edge detection) for 2.7 SHAPE DESCRIPTOR (CSD) EVALUATIONminimising the surrounding effects. Once the zero cross were obtained they are buffered for a corresponding arc length (u) and given2.2 CANNY EDGE DETECTION Gaussian value (σ), once all the zero cross were The compressed and smoothened image is found they are been plotted for arc length v/s sigma.passed as input for canny edge detection unit. This From the obtained CSD plot important curvatureedge detection is performed using defined canny edge features are extracted. To obtain the importantoperators. Once the preprocessing operation is done curvature feature a threshold is set given bythe obtained edge information is passed for CSD Threshold (T) = 0.6 * max. Peak value. This indicatesevaluation, where the initial operation is to evaluate that a CSD peak of more than 60% of the obtainedthe contour of the given edge information. curvature information is used for image feature representation. This approach eliminates the2.3 BOUNDARY EXTRACTION consideration of lower peaks resulting in elimination Contour is defined as outermost continuous of shape information generated due to externalbounding region of a given image. For the detection noises. This noise used to be reduced by filtrationof contour evaluation all the true corners should be approach in conventional methods.detected and no false corners should be detected. Allthe corner points should be located for proper 2.8 FEATURE EXTRACTION ANDcontinuity. The contour evaluator must be effective in RECOGNITION:estimating the true edges under different noise levels Once the CSD plot is obtained, features arefor robust contour estimation. For the estimation of extracted from the selected CSD peaks, then groupedthe contour region an 8-region neighbourhood- together for matching and classification, given asgrowing algorithm, once the contour is detected the Feature={(σ1,arclength1), (σ2, arclength2)…(σn,curvature for the obtained contour is calculated. arclengthn)},Where n= no. of peaks crossing the2.4 CURVATURE EVALUATION threshold limit. This feature extraction is done for the To evaluate the curvature for the obtained given database information and query image whichcontour of given image following approach is made. are used for recognition. Once the knowledge isFor a given a contour co-ordinates (p (u), q(u)) the created the recognition operation is performed.curvature of the given contour is given by 𝑝′ (𝑢 )∗ 𝑞′′ (𝑢 ) – 𝑞′ (𝑢) ∗ 𝑝′′ (𝑢) 3. EXPERMENTAL RESULTS AND C (u) = [( p′ (u ))^2 + (q′ (u))^2]^(3/2) PERFORMANCE ANALYSIS Where (p’, q) are first derivative of given contour highboost image2 3.1.RESULTSco-ordinates and (p’’, q) are the double derivative of Original Imagep and q.C (u) is the curvature of the given image.For the obtained curvature, CSD is obtained byapplying smoothening operation to reduce the zerocrossing co-ordinates in bounding contours. Thesmoothening is continued by incrementing theGaussian value (σ) on the obtained contour until nozero crossing exists. (a) (b)2.5 CURVATURE SMOOTHENING 20 18 CSS-final Recognized ImageThe curvature smoothening is done using equation 16 14 𝑃’ (𝑢 ,𝜎 )∗𝑄’’ (𝑢,𝜎 )−𝑄′ (𝑢,𝜎 )∗𝑃′′ (𝑢 ,𝜎 )C (u, σ) = [(P′ (u,σ))^2 + (Q′ (u,σ))^2]^(3/2) 12 10 8 Where e, P = conv (p, g) and Q = conv (q, g), 6 4g is Gaussian distribution function and u is the arc 2 0 5 10 15 20 25 30 35 40 45 50length parameter (c) (d) (e) 2134 | P a g e
  4. 4. A. Jwalitha, M.V.Srikanth / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue4, July-August 2012, pp.2132-2135 accuracy of CSD high, when compared to Invariant- Moment based method and also CSD method requires less retrieval time which is compared to Invariant-Moment based method for retrieving the same image shown in figure 3.2.(f) 4 Comparision of retrieval time factor between CSD and Invarient method Invr-Method Invarient based Recognition 3.5 CSD-Method 3 Computation time (sec) 2.5 2 1.5 1 (g) 0.5Fig 3.1 (a) input image (b) test image subjected to 0 1 Methods 2compression,blurring and rotation,(c)CSD plot Fig3.2 Comparision of Recogntion Accuracy factor between CSD and Invaient method(d)classified images in CSD method (e) CSD based 300 Invr-Method CSD-Methodrecognized image (f)classified images in Invarient 250moment method (g) Invarient method based 200 recognition levelrecognition 150 A trademark query image shown in Fig 1003.1(a) is subjected to blurring and is 25% compressed 50and rotated at 90 degrees as shown in Fig 3.1(b) 0 1 method 2which is passed to the algorithm for retrieval purpose. Fig3.3Based on the features of CSD shown if Fig 3.1(c) and 4. CONCLUSIONthe moments (M1 to M7) of Invariant moment An efficient shape-based retrieval algorithmmethod, Classification has done and shown in Fig has been developed to retrieve image which have3.1(d) and Fig 3.1(f) respectively. From Fig 3.1(e) shown to be a promising technique for shape-basedand Fig (g), it can be observed that the CSD method image database retrieval. The CSD based method isis again more efficient in retrieving images when it is compared with the invariant based estimationblurred and compressed and when subjected to noise algorithm and observed to be robust underand orientation changes. compression, blurring, rotation, and noisy versions of the database images. Further it is observed that the3.2 PERFORMANCE RESULTS CSD based estimation outperforms the existing The Figures given below specifies techniques with higher accuracy and lessperformance analysis between CSD and Invariant classifications for retrieval of an image.Moment based methods under compression, blurringand compressed with noise corrupted and subjectedto orientation cases. figure 3.3 shows that retrieving [5] Mahmoud R. Hejazi, Yo-Sung Ho, AnREFERENCES Efficient Approach to Texture-Based [1] Sadegh Abbasi, Farzin Mokhtarian, Josef Image Retrieval ,Vol. 17, 295–302 Kittler , Curvature scale space image in (2007), 2008 Wiley Periodicals, Inc. shape similarity retrieval, Centre for [6] Rishav Chakravarti, Xiannong Meng, A Study Vision Speech and Signal Processing, of Color Histogram Based Image Department of Electronic & Electrical Retrieval, Sixth International Conference Engineering, University of on InformationTechnology2009: New SurreyGuildford GU2 5XH, UK; Generations, IEEE [2] Anil K. Jain and Aditya Vailaya, shape- [7]Faisal Bashi, Shashank Khanvilkar, Ashfaq based retrieval – A case study with Khokhar, and Dan Schonfeld, Multimedia trademark image databases, Dept of Systems: Content Based Indexing and computer science, Mitchigan University. Retrieval. [3] W. Niblack et al, The QBIC project; [8] R.C. Gonzalez and R.E. Woods, Digital querying images by content using colour, Image Processing (Addison Wesley texture and shape, in SPIE, VOL. 1908, Publications). 1993. [9] William K. Pratt. Digital Image processing [4] Yossi Rubner and Carlo Tomasi, Texture- (John Wiley and Sons, Inc, New York, Based Image Retrieval without NY, second edition, 1991). Segmentation, Computer Science [10]Anil K. Jain, Fundamentals of Digital Image Department ,Stanford University, Processing (Prentice-Hall, Englewood Stanford, CA 94305. Cliffs, NJ, 1989). 2135 | P a g e

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