Content-Based Image Retrieval using Color and Shape ... - IEEE Xplore

Content-Based Image Retrieval using Color and Shape Descriptors Jagadeesh Pujari , Pushpalatha S.N

Padmashree D.Desai Dept. of CSE,

Dept. of ISE, SDM Engg., College, Dharwad [email protected], [email protected]

Dept., of CSE, BVB Engg. College. Hubli [email protected]

Abstract

2. RELATED WORK

Content-Based Image Retrieval technique uses three primitive features like color, texture and shape which play a vital role in image retrieval. This paper presents a novel framework using color and shape features by extracting the different components of an image using the Lab and HSV color spaces to retrieve the edge features. Invariant moments are then used to recognize the image. In this proposed work, the performance of the HSV and Lab color space approach have been compared with Gray and RGB approach[11]. Accordingly the Lab color space approach gives better performance than RGB and HSV. The experiments carried out on the bench marked Wang’s dataset, comprising Corel images, demonstrate the efficacy of this method.

Shape is an important feature for perceptual object recognition and classification of images. It has been used in CBIR in conjunction with color, texture, and structure for retrieval. Shape description or representation is an important issue both in object recognition and classification. Many techniques including the chain code, characteristics, circumference, area, and circular degree have been proposed and used in various applications [2]. A.Grace Selvarni and Dr.S.Annadurai [3], used generic fourier shape descriptor technique in image retrieval. The computational rate is high. Sami Brandt, Jorma Laaksoner and Krkki oja [4] used a combination of edge histograms and fourier transform for computing edge image in Cartesian and polar coordinate plans. The feature set provides clues of shapes in an image. X.Fu, Y.Li, R.Harrison, S.Belkasim [5], used a combination two lowlevel features, that is texture and shape, to extract the image by combining the Gabor filter and Zernike moments on the database MPEG-7. The results showed robustness but both GF and ZM are limited for certain databases. Li jun, jiangyong shi [6], used a mesh simplification method based on shaped feature which only improve the quadratic error metric of vertex pair contraction by preserving the detailed feature of the models and gives a detail shape description. Fuhui long, Hongjang Zhang and David Dagan Feng[7], used fundamentals of content-based image retrieval which give a wide range of description of shape feature based both on boundary-based as well as region-based categories. M.Banerjee, M.K.Kundu, P.K.Das [8], used visually prominent features using fuzzy set theoretic evaluations, where clusters of points around significant curvature region(high, medium, weak type) are extracted to obtain a representative image. Illumination viewpoint invariant color features are computed from those points for evaluating similarity between images. Wang Xiaoling, Xie Kanglin[9], used the application of the fuzzy logic in content-based image retrieval, where the firstly adopted the fuzzy language variables to describe the similarity degree, not the feature themselves; secondly making use of the fuzzy inference to instruct the weights assignment among various image feature; thirdly expressing the subjectivity of human perceptions by fuzzy rules impliedly. Serge Belongie, Jitendra Malik and Jan Puzicha [10], used shape matching and object recognition with respect to a context; where the shape context at a reference point captures the distribution

Keywords: Color, Shape, Lab space, HSV

1. INTRODUCTION In many areas of commerce, government, academia, and hospitals, large collections of digital images are being created. Many of these collections are the product of digitizing existing collections of analogue photographs, diagrams, drawings, paintings, and prints. Usually, the only way of searching these collections was by keyword indexing, or simply by browsing. Digital images databases however, open the way to content-based searching. Image retrievals using text-based key-words are tedious as well as churn out results that may be irrelevant. Retrieval of images using their content (features) is called Content Based Image Retrieval. The query that may be presented to such a system may be an image that the user has, a rough sketch, a color or texture layout or a short verbal description. Characteristics of CBIR include: • Image retrieval by image content • Visually similar images to Query image • No keywords • Low level features like color, texture and shape are used However the main challenge involved in image retrieval by content is the need to bridge the semantic gap between lowlevel features and high-level concepts. The need to manage huge images and to locate target images in response to user queries poses a significant problem for research in Digital Image Processing.

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of the remaining points relative to it, thus offering a globally discriminative characterization. This paper uses color and shape information which is captured in terms of edge images computed using Lab color space and HSV techniques using the gradient technique is shown to be effective in retrieval.

3. SYSTEM OVERVIEW AND PROPOSED METHODOLOGY The methodology involves the study of Gradient, Lab and HSV color space are used for the implementation of CBIR using color and shape descriptor approach. Fig. 3.1 represents the query image and fig 3.2 represents the edge image of the query image. The core methodology arises from the following two points: • •

The mean measures the average gray level in an image characteristic in a predefined region (Horizontal x-direction and vertical y-direction) about each pixel in the image. The mean computation is given by: Mean(k1) = (f(x))1/2 ……………... (1) Mean(k2) = (f(y))1/2 ……………... (2) The minima measure the average contrast. The minima computation is given by: Minima1 = 1.5626 Mean (k1) ……... (3) Minima2 = 1.5626 Mean (k2) ……... (4) The 1.5626 is a constant empirically determined. Compare the absolute value of f(x) with the minima1 store the strong edge on x-direction and compare the absolute value of f(y) with the minima2 store the strong edge on ydirection. Masking (bitwise OR) both the directions to obtain a sharp edge of the image. 3.2 LAB COLOR SPACE

Extraction of shape features Retrieval of similar image from image database.

Fig. 3.1 Image from the database

Fig. 3.2 Edge image to be extracted

Fig. 3.2.1 3.1 GRADIENT METHOD An image is a function of two variables f(x, y). If an image has a black color than the pixel is assigned a value zero. If an image has a white color than the pixel is assigned a value one. For pixels with grayish colors, a value between zero and one is assigned depending on the brightness of that pixel. An edge in an image is a region, which has sharp contrast that is rapid change in color intensity. A rapid change in a function gives a large magnitude of the gradient at edges. The gradient is a geometric computing method for characterizing symmetric breaking of an ensemble of asymmetric vectors regularly distributed in a square lattice. The gradient method detects the edges by looking for the maximum and minimum in the first derivative of the image.

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A Lab color space is a color-opponent space with dimension L for lightness and a and b for the coloropponent dimensions, based on nonlinearly compressed CIE XYZ color space coordinates. for the CIE 1976 (L*, a*, b*) The three coordinates of CIELAB represent the lightness of the color (L* = 0 yields black and L* = 100 indicates diffuse white; specular white may be higher), its position between red/magenta and green (a*, negative values indicate green while positive values indicate magenta) and its position between yellow and blue (b*, negative values indicate blue and positive values indicate yellow) 3.3 HSV COLOR SPACE

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Hue In HSV, hue represents color. In this model, hue is an angle from 0 degrees to 360 degrees. Saturation Saturation indicates the range of grey in the color space. It ranges from 0 to 100%. Sometimes the value is calculated from 0 to 1. When the value is '0,' the color is grey and when the value is '1,' the color is a primary color. A faded color is due to a lower saturation level , which means the color contains more grey. Value Value is the brightness of the color and varies with color saturation. It ranges from 0 to 100%. When the value is '0' the color space will be totally black. With the increase in the value, the color space brightness up and shows various colors. Feature Extraction ‡ƒ–—”‡•ƒ”‡‡š–”ƒ…–‡†—•‹‰ƒ„ƒ†…‘Ž‘”•’ƒ…‡•Ǥ In Lab color space, RGB image is converted to get the luminous(L) component and Chrominance (a & b) components. The L component is used to compute sharp edges of the image using gradient approach. Lab color space approach extracts nine features – seven moment invariants for shape and two statistical moments for color. In HSV color space, the RGB image is converted to get the Brightness value (V) component and Chrominance (H & S) components. The V component is used to compute sharp edges of the image using gradient approach. HSV approach extracts nine features - seven moment invariants for shape and two statistical moments for color.

Algorithm for finding gradient edge detection in HSV Color space . Step1. Get H, S and V components of the image. Step2. Compute gradient map of the image using the V component. Step3. Filter out the strong edge responses using k, where k is the standard deviation of the gradient. Step4. Dot product of the gradient map with the binary image Step5. Edge information will be obtained which converge onto edge pixels. Step6. Feature set for the query image using moment invariants is calculated. 4. EXPERIMENTAL SETUP a) Data set: The image set comprises 500 images segregated in 05 categories. The images are of various shapes and sizes. b) Feature set: The Feature set comprises Color and shape descriptors. Canberra distance measure is used for similarity comparison in all the cases. It allows the feature set to be unnormalized form. The Canberra distance measure is given by:

Where x and y are the feature vectors of database and Query image, respectively, of dimension d. 5. EXPERIMENTAL RESULTS

The color and shape information is captured in terms of the edge image of the Lab color space and HSV equivalent of every image in the database. The gradient method is used Algorithm for finding gradient edge detection in Lab color space: to obtain the sharp edge images. The performances of the HSV and Lab color space approach have been compared Step1. Get L, a and b components of the image. with Gray and RGB approach [11]. Accordingly the Lab Step2. Compute gradient map of the image color space approach gives better performance than RGB using the L component. and HSV as illustrated in the fig. 5.1. The results for Lab Step3. Filter out the strong edge responses color space and HSV color space are illustrated in Table 1 using k, where k is the standard and Table 2 respectively. deviation of the gradient. Step4. Dot product of the gradient map with the binary image Step5. Edge information will be obtained which converge onto edge pixels. Step6. Feature set for the query image using moment invariants is calculated.

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TABLE 1 Performance of Lab color space

Category/no of retrievals Roses Monuments Buses Elephants Dinosaurs

REFERENCES

10

20

25

100% 85.6% 85% 84% 95.2%

96.75% 80% 80.7% 73.4% 90.7%

96% 74.8% 74.2% 68.45% 88.4%

. TABLE 2

Category/no of retrievals Roses Monuments Buses Elephants Dinosaurs

Performance of HSV color space

10

20

25

100% 79.5% 83% 82.6% 94%

95.75% 74.5% 80% 70% 88.6%

94.6% 73.8% 73.8% 66.2% 86.4%

120 100 80

Lab Space HSV

60 40 20 0 Di no sa ur s

Bu se s

El ep ha nt s

M

on um en ts

RGB

Ro se s

Percentage retrieval

Performance Analysis of RGB,HSV, Lab space

Image Category

Fig. 5.1

Performance Analysis of RGB, HSV and Lab color space

6. CONCLUSION

[1]

M. K. Hu. Pattern recognition by moment invariants, Proc.IRE, 49, 1961. [2] Yong-xianga sun, cheng-minga zhang, pingzenga liu, hong-mei zhu,”The shape feature extraction based on chain code”,Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition,Bejing,china,2-4 Nov.,2007 [3] A. Grace Selvarni and Dr.S.Annadurai, A “Contentbased medical image retrieval for medical images using generic fourier descriptors”, Journal of computational Intelligence in Bioinformatics, ISSN: 0973-385X Volume 1 Number 1(2008) pp. 65-72. © Research Indian Publications [4] Sami Brandt, Jorma Laaksoner and krkki oja, A “Statistical Shape Features in Content-based Image Retrieval”, © 2000 IEEE. [5] X.Fu, Y.Li, R.Harrison, S.Belkasim,A “Contentbased Image Retrieval Using Gabor-Zernike Feature”, The 18th ICPT ’06 IEEE. [6] Li jun,jiangyong shi,A.,”A Mesh Simplification Method Based On Shape Feature”,ICSP 2006 proceedings. [7] Dr.Fuhui long,Dr..Hongjang Zhang and Prof. David Dagan Feng[,A.,” Fundamentals Of Content-Based Image Retrieval “. [8] M.Banerjee,M.K.Kundu,P.K.Das[7], A.,”Image Retrieval With Visually Prominent Features Using Fuzzy Set Theoretic Evaluation”. [9] Wang Xiaoling, Xie Kanglin,A., “Application Of The Fuzzy Logic In ContentBased Image Retrieval”,JCST&Vol. 5 No. 1, April 2005. [10] Serge Belongie, Jitendra Malik and Jan Puzicha ,A.,”Shape Matching and Object Recognition Using Shape Contexts”. [11] Jagadeesh Pujari, Pushpalatha S.N, Padmashree Desai “Content-based image based retrieval using Regionbased shape descriptor”, vol 1, page 131-139 @ IEEE-ACVIT’09 Aurangabad, India.

A general approach to retrieve the images from color image database has been proposed. Comparison of Gray- RGB, HSV, and Lab color space are illustrated with the aid of graphs. The test cases show that Lab color space approach gives better performance than RGB and HSV approach. The performance was reasonable as per the graph shown in the fig. 5.1. This paper concentrated on maximum retrieval of all those images that match the query image from the given database.

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2010 International Conference on Signal and Image Processing