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Poster Paper Proc. of Int. Conf. on Advances in Computer Engineering 2011

Retrieval of Color Image using Color Correlogram and Wavelet Filters Dipankar Hazra Dr.B.C.Roy Engineering College, Durgapur, India Email: dipankar1998@rediffmail.com pixels. Local histogram of each cell of query image is computed and compared with local histogram of same cell of database image. Another approach of color based image retrieval is color texture moments [3]. But Color Correlogram [4]-[8] outperform all the previous approaches. Texture features produce perfect result for regular textured color images. Hence we also incorporate texture feature for image retrieval where color correlogram fails. Among the texture analysis method gray level co-occurance matrix or GLCM by Haralick [9], gabor wavelet [10], Haar wavelet [11], Daubechies wavelets [12] are widely used. Rotated wavelet filters [12], [13] are constructed from Daubechies wavelets and used with Daubechies wavelet to extract the diagonal textures and increase the retrieval accuracy of the wavelet. Ref. [14] shows that combination of color of texture features can be used as efficient and accurate image retrieval technique.

Abstract- Objective of this paper is to develop a color image indexing and retrieval system using color correlogram and wavelet filters. Color, Texture and shape are the three main features of image retrievals. Among them color feature is mainly used for color image retrieval. Color correlogram is used as the color feature in this paper. It gives better result than another commonly used color feature such as color histogram. Daubechies 4 wavelet is used for texture based image retrieval. Rotated Wavelet Filter recognizes diagonal textures better than Daubechies 4 Wavelet Filter. In this paper combined wavelet approach is used as texture feature to increase the retrieval accuracy of the algorithm. It distinguishes the color image with regular texture feature. Experimental result demonstrates that combination of color correlogram and wavelet filters gives more accurate result than using color correlogram for image retrieval and browsing. Keywords: Content based image retrieval, image indexing, color correlogram, autocorrelogram, rotated wavelet filter, color retrieval, texture retrieval.

II. RELATED WORKS Histogram does not give spatial correlation among color changes. But color correlogram expresses different colors and how spatial correlation of color changes with distance. But same color different texture images color correlogram fails. Hence Wavelet Filter, which is popular for its MRA capability, is used for texture recognition in those cases. Wavelet Features are proved to be more accurate than Tamura and Haralick features. Rotated Wavelet Filter is used for diagonal texture recognition.

I. INTRODUCTION Content Based Image Retrieval is one of the most powerful research areas nowadays. Searching a database of millions of images is a difficult task and lots of time is required for that. To reduce the search time one can use indexes like the indexes to search in the book. Different features are used for indexing and retrieval of images. Color, texture and shape are the most powerful features. Feature vectors of various types of images are calculated and stored as indexes of the images. Feature vector of the query image is calculated and compared with those index values for nearest image matching. Still any of the existing method can not give complete accuracy. This work tries to improve the accuracy of the existing method. For color images with irregular texture color is the most important feature used for indexing and retrieval. There are different method for color feature based image retrieval. Pixel to pixel comparison was initially used as color based image retrieval. But this approach was computation expensive and very sensitive to camera and light position. Global histogram processing [1] is a popular approach. A histogram is a plot of the number of pixels belonging to each specific set of colors. If the difference between global histogram of query image and same of database image falls below certain threshold, the images are considered to be similar. Major drawback of global histogram processing is it does not give any information about spatial distribution of colors. Local histogram processing [2] gives information about spatial distribution of colors. Images are partitioned into fixed number of rectangular cells containing fixed number ÂŠ 2011 ACEEE DOI: 02.ACE.2011.02.123

A. Color Correlogram Correlogram can be stored as a table indexed by pairs of colors (i,j) where d-th entry shows the probability of finding a pixel j from pixel i at distance d. Whereas an auto-correlogram can be stored as a table indexed by color i where d-th entry shows the probability of finding a pixel i from the same pixel at distance d. Hence auto-correlogram shows the spatial correlation between identical colors only. Experiment shows that correlogram and auto-correlogram both are computational expensive. Hence we use correlogram with small number of color and distance value which still yields very good result without increasing the computational cost. Let [D] denote a set of D fixed distances {d1,,â&#x20AC;Ś, dD}. Then d the correlogram ď § c c ( I ) of the image I is defined for color i, j

pair (ci, cj ) at a distance d. (1) 151

Poster Paper Proc. of Int. Conf. on Advances in Computer Engineering 2011

The auto-correlogram

 cdi ( I ) of the image I is defined for

color Ci at a distance d. The image is divided into 4 subbands by applying Daubechies 4 Filter. These subbands are labeled LL1(LowLow), LH1(Low-High), HL1(High-Low) and HH1(High-High). LH1, HL1, and HH1 are mostly sparse as the most of the images are low-frequency oriented. LL1 band further decomposed to get next level of wavelet coefficients. This results into two level wavelet decomposition as shown in Fig.2. This process is continued until some final scale is reached. Energy of all subbands at multiple resolutions combined to calculate feature vector of the image.

Example: Two sample images of size 4x4 are shown in Fig.1. 2color 2-distance correlogram matrix for image1 and image 2 is shown in Table I. Though histograms of both images are same, due to different spatial distribution correlogram of two images are different.

Rotated Wavelet Rotated wavelet filters can be constructed by rotating Daubechies 4 wavelet filters. Low-High and High-Low subbands of Rotated Wavelet Filters contain diagonal information. This diagonal information along with horizontal and vertical characteristics obtained from standard Daubechies 4 wavelets give higher retrieval rate in texture recognition application. Energy of these two subbands at multiple resolutions has been considered.

Fig. 1 Sample images: image1, image2 TABLE I. 2-COLOR 2-DISTANCE CORRELOGRAM MATRIX

III. PROPOSED APPROACH Proposed approach can be divided into two phase, storing phase and query phase. A.. Storing Phase For an 8 bit RGB image, the number of possible colors are (28)3=16,777,216. Calculating correlogram for so many colors are space and time consuming. Hence the RGB image is converted into indexed image. An indexed image is consisting of two components: a color map matrix and a data matrix of integers. The length of the color map matrix is equal to number of color it defines. In our case it is 8. Each row contains three columns for Red, Green and Blue values for pixel. Data matrix use as an index to the color map matrix. For image of size 64x64, maximum distance is 63 between two pixels. But it has been seen local correlation is more important than correlation with larger distances. Hence to reduce the space and time complexity correlogram is calculated is for 8 colors and 8 distances. The size of the color correlogram is O(m2d). In this case the size is (82)8=512. This can be stored as a feature vector of size 512.

Fig.2 Sample 2-level Image Decomposition

Daubechies Wavelet Wavelet is used for detecting texture of an image. The most known family of orthogonal wavelets is a family of Daubechies. Daubechies’ wavelets are more popular due to their relations to multiresolution analysis (MRA). Coarse texture patterns manifests peaks at the lower frequencies. The fine texture patterns manifest peaks at the higher frequencies of the spectrum. The spectrum also indicates the directionality of the textures. The implementation of a wavelet is possible using two kinds of filters-g(high pass) and h(low pass). A pair of filters is used to divide the frequency in subbands. This is repeated recursively till the lowest frequency band of the image is reached. Downsampling is used to ensure that the size of the input image remain same. The basis vectors of Daubechies 4 wavelet are

Algorithm: m-color n-distance Correlogram as onedimensional feature vector For i=1 to m*m*d {Initialize Feature Vector F[i]=0;} For every X position in Image { For every Y position in Image { Val1=Value of current pixel; Count (Val1) = Count(Val1)+1; For Neighbor_of_X=(X-d) to (X+d){ For Neighbor_of_Y=(Y-d) to (Y+d){ Distance=Max(|X-neighbor_of_X|+ |Y-neighbor_of_Y|); Val2=Value of Neighbor Pixel; If (Distance>0){ 152

Poster Paper Proc. of Int. Conf. on Advances in Computer Engineering 2011 F[(Distance-1)*m*m+Val1*m+Val2+1) = F[(Distance-1)*m*m+Val1*m+Val2+1)+1

Query Phase The feature vector of the query image is compared with the feature vector of the database image. Let x and y are ddimensional Feature vectors of two images. x = [x1 x2 x3 ………xd] y = [y1 y2 y3 ……….yd] Distance between x, y is calculated using Canberra distance [12]. Canberra distance is given by,

} } } }; For Distance= 1 to d{ For Val1=0 to m{ For Val2=0 to m{ F[(Distance-1)*m*m+Val1*m+Val2+1) = F[(Distance-1)*m*m+Val1*m+Val2+1) /count(Val1+1); } } }

The numerator denotes the difference value, whereas denominator normalizes the difference. The difference value never exceeds one. If the distance between feature vector of the query image and feature vector of an image from the image database is falls below certain threshold we call the query image is similar to the database image. A query image may be similar to many database images. If more than certain percentages of images are of same type, we say query image is an image of that specific type. If distance between query image and any image of image database does not fall below threshold or ratio of one similar type of images and all similar type of images is less than certain percentage then the query image is not properly classified. In this case attempt is made to classify it by texture recognition in a same way as color based recognition.

For texture feature intensity values I of the pixels are calculated using red r , green g and blue b values of pixels using following formula, Discrete Wavelet Transform of each image is performed with the intensity values up to 5 th level. Energy of each subbands (LL, LH, HL, HH) are calculated and stored in feature vector. Similarly Rotated Wavelet Transform of each image is performed up to 5 th level. Energy of LH and HL subbands are calculated and stored in the feature vector. Hence length of the feature vector is 5x4 (For DWT) + 5x2 (For RWF) = 30. The formula of calculating energy of a subband is,

Fig.3 Data Flow Diagram of proposed approach

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Poster Paper Proc. of Int. Conf. on Advances in Computer Engineering 2011 IV. EXPERIMENTAL RESULT

REFERENCES

VisText color image database [15] is used for experiment. Images downloaded from the online Google search engine with various keywords in various categories such as sky, water are also included for forming image database. Total no of images used in this experiments are 84 and 10 categories of images are used. 21 sub images of size 64×64 are obtained from each image to form the color image database. Various categories are: sky, water, brick, bark, grass, sand, food, flower, tile and fabric. Query image of this 10 different categories are tested to check if they are falling on the same category or not. Table II shows that combination of color correlogram and rotated wavelet filter approach gives better accuracy than only color correlogram approach. But for different categories of images accuracy rate is different. It is demonstrated in Table III.

[1] M Swain, D Ballard, “Color Indexing”, Int. Journal of Computer Vision, Vol. 7(1), 1991, pp. 11-32. [2] Y Gong, H C Chua, X Guo, “Image Indexing and Retrieval Based on Color Histogram”, Proc. of the 2nd International Conference on Multimedia Modeling, Singapore, Nov.1995, pp. 115-126. [3] Yu H., Li M., Zhang H. and Feng J., “Color texture moment for content based image retrieval”, Proc .IEEE Intl Conf. on Image Processing, September, 2002. [4] J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu, R. Zabih, “Image Indexing Using Color Correlograms”, Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition, 1997, pp. 762-768. [5] J. Park, S. Han, Y. An, “Heuristic Features for Color Correlogram for Image Retrieval”, Proc. International Conference on Computational Sciences and Its Applications ICCSA, 2008. [6] W Rasheed, Y An, S Pan, I Jeong, J Park, “Image Retrieval using Maximum Frequency of Local Histogram based Color Correlogram”, International Conference on Multimedia and Ubiquitous Engineering, 2008. [7] I. Kunttu, L. Lepisto, A. Visa, “Image Correlogram in Image Database Indexing and Retrieval”, Proc. 4th European Workshop on Image Analysis for Multimedia Interactive Services,2003. [8] A. Tungkasthan, S. Intarasema, W. Premchaiswadi, “Spatial Color Indexing using ACC Algorithm”, 7 th International Conference on ICT and Knowledge Engineering, 2009. [9] R.M. Haralick, K. Shanmugam, I. Dinstein, “Textural Features for Image classification”, IEEE Transaction on Systems, man and Cybernetics, Vol. SMC-3, 1973, pp.610-621. [10] Manjunath B.S, Ma W.Y, “Texture features for browsing and retrieval of image data”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No. 8, Aug,1996. [11] Q Tian, N Sebe, M S Lew, E Loupias, T S Huang, “ Image Retrieval using wavelet-based salient points”, Journal of Electronic Imaging, Special Issue on Storage and Retrieval of Digital Media, vol.10(4),Oct, 2001, pp.835-849. [12] M. Kakore, P.K. Biswas, B.N. Chatterjee, “Texture image retrieval using rotated wavelet filters”, Pattern Recognition Letters 28, 2007, pp. 1240-1249. [13] Dipankar Hazra, “Texture Recognition with combined GLCM, Wavelet and Rotated Wavelet features”, Int. Journal of Computer and Electrical Engineering, Vol.3, No.1, Feb,2011. [14] V. Balamurugan, P.AnandhaKumar, “An Integrated Color and Texture Feature Based Framework for Content Based Image Retrieval Using 2D Wavelet Transform”, Proc. International Conference on Computing, Communication and Networking, 2008. [15] MIT Vision and Modeling Group., “Vistex: Vision texture database,” 1995.

TABLE II. ACCURACY TABLE FOR DIFFERENT METHOD

TABLE III. ACCURACY TABLE FOR DIFFERENT CATEGORIES OF IMAGES

V. CONCLUSION In this work combination of color and texture feature are used for image recognition. This work proves that combined features (color, texture) gives better result than single features to capture individual properties of single object in image. For color based recognition color correlogram is used and for texture based recognition energy feature of standard Daubechies 4 wavelet and rotated wavelet are used. Color correlogram considers spatial relationship of colors which is the major drawback of histogram method. Rotated Wavelet Filter is used to extract directional texture information along with horizontal and vertical texture extracted by standard Daubechies 4 wavelets. Further research can be done by incorporating extra features. Also different distance measures can be compared to improve accuracy of this approach.

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