Image Retrieval Based on Multi-features

Kong Fanhui Department Of Information Science & Technology , Heilongjiang University, Harbin 150080,China

E-mail: kongfh2000@126.com

Abstract—This paper studies the visual feature extraction of

image retrieval. According to HSV color space, we quantify

the color space in non-equal intervals, construct

one-dimensional feature vector and represent the color

feature by cumulative histogram. In describing the image

texture features, we use the gray-level co-occurrence matrix

(GLCM) and Gabor wavelets respectively. Finally, the HSV

color features are combined with GLCM and Gabor

wavelets respectively for image retrieval. Experiment results

show the effectiveness of the algorithm.

keyword- Image retrieval; GLCM; Gabor wavelet

I. INTRODUCTION

Content-based image retrieval is a technology of using image color, texture, shape, spatial relationship and other features for retrieval. Using single feature for image retrieval is not a good solution for the accuracy and efficiency. High-dimensional features will reduce the query efficiency; at the other hand, low-dimensional features will reduce query accuracy, so it may be a better way using multi-features for image retrieval [1, 2]. Color and texture are the most important visual image features. Firstly, we discuss about the color and texture features, and then combine the color characteristics with GLCM and Gabor wavelet texture features for image retrieval, respectively. Finally, experiments are done on the images database, satisfactory result are achieved and verify the superiority of integrated feature than the single feature.

II. FEATURE EXTRATION OF HSV COLOR SPACE

The advantage of HSV color space is that it is closer to human conceptual understanding of color and has the ability to separate chromatic and achromatic components. In the extraction of color features, the image is converted into HSV color space from the RGB color space. Based on the color model of substantial analysis, we divide color into eight parts. Saturation and Value is divided into three parts separately in accordance with the human eyes to

distinguish. In accordance with the different colors and subjective color perception quantification, quantified hue (H), saturation(S) and intensity (V) are showed as equation (1).[3]

⎪⎪⎪⎪⎪

⎩

⎪⎪⎪⎪⎪

⎨

⎧

∈∈∈∈∈∈∈∈

=

]315,296[ 7]295,271[ 6]270,191[ 5]190,156[ 4

]155,76[ 3]75,41[ 2]40,21[ 1

]20,316[ 0

hifhifhifhifhifhifhifhif

H

（1）

[ )[ )[ )⎪

⎩

⎪⎨

⎧

∈∈∈

=1,7.0 2

7.0,2.0 12.0,0 0

sifsifsif

S

[ )[ )[ )⎪

⎩

⎪⎨

⎧

∈∈∈

=1,7.0 2

7.0,2.0 12.0,0 0

vifvifvif

V

In accordance with the quantization level above, the H, S, V feature vector for different values with different weight to form one-dimensional feature vector named G:

VSQHQQG VVS ++= （2） Where SQ and VQ are the components and the

quantitative series of G: VSHG ++= 39 （3）

We construct cumulative histograms as image color feature representation. This will not only solve the zero value on the impact of similarity measure, and enhance the robustness by the way of accumulating its histogram.

III. TEXTURE FEATURE EXTRATION

A. Texture feature extraction based on GLCM

GLCM texture is one of the statistical analysis methods, it can more accurately reflect the direction of texture roughness. GLCM is composed of the probability

value, it is defined by ( ), ,P i j d θ which expresses the

2011 International Conference on Network Computing and Information Security

978-0-7695-4355-0/11 $26.00 © 2011 IEEE

DOI 10.1109/NCIS.2011.87

396

2011 International Conference on Network Computing and Information Security

978-0-7695-4355-0/11 $26.00 © 2011 IEEE

DOI 10.1109/NCIS.2011.87

398

probability of the couple pixels at θ direction and d interval. When θ and d is determined,

( )θ,, djiP is showed by jiP , . Distinctly GLCM is a

symmetry matrix; its level is determined by the image gray-level. Elements in the matrix are computed by the equation showed as follow:

( ) ( )( )∑∑

=

i j

djiPdjiP

djiPθ

θθ

,,,,

,, (4)

GLCM expresses the texture feature according the correlation of the couple pixels gray-level at different positions. It quantificationally describes the texture feature. In this paper, four features is selected, include energy, contrast, entropy, inverse difference.[4]

Energy 2( , )x y

E p x y=∑∑ (5)

It is a gray-scale image texture measure of homogeneity changing, reflecting the distribution of image gray-scale uniformity of weight and texture.

Contrast 2( ) ( , )x y

I x y p x y= −∑∑ (6)

Contrast is the main diagonal near the moment of inertia, which measure the value of the matrix is distributed and images of local changes in number, reflecting the image clarity and texture of shadow depth. Contrast is large means texture is deeper.

Entropy ( , ) log ( , )x y

S p x y p x y= −∑∑ (7)

Entropy measures image texture randomness, when the space co-occurrence matrix for all values are equal, it achieved the minimum value; on the other hand, if the value of co-occurrence matrix is very uneven, its value is greater. Therefore, the maximum entropy implied by the image gray distribution is random.

Inverse difference 2

1 ( , )1 ( )x y

H p x yx y

=+ −∑∑ (8)

It measures local changes in image texture number. Its value in large is illustrated that image texture between

the different regions of the lack of change and partial very evenly.

Here ),( yxp is the gray-level value at the

coordinate ),( yx .

In order to obtain rotation invariant texture feature, we firstly quantify gray into 32 levels and calculate the

co-occurrence matrix where 135,90,45,0=θ ,

then get the five kinds features of GLCM (energy, moment of inertia, entropy, inverse difference), finally find each feature vector of the mean and standard deviation as the texture feature vector of each component.

This algorithm to extract image texture feature vector is:

[ ]HHSSIIEET σμσμσμσμ ,,,,,,,=

B. Texture feature extraction based on Gabor wavelet

Frequency and direction of variability of Gabor filters is especially useful in texture analysis.[5,6,7]

The general form of basic functions of Gabor filter is as follows:

σ

πσ2

)(

221),(

yx

eyxg+−

= (9)

A two-dimensional Gabor function along the x-axis of the complex sine wave is:

)2sin2(cos2

1),( 22)(

2 lxj

lxeyxG

yx πππδ

δ +=+−

(10)

We see ),( yxG as the generating function and its

transform it by rotation and scale, so we can get a set of filters:

1),,(),( '' >= − ayxgayxg mmn (11)

where,

]),,0[(,/),cossin(

),sincos('

'

knknyxay

yxaxm

m

∈=⋅+⋅−=

⋅+⋅=−

−

πθθθ

θθ (12)

m is the scale number, n represents the number of direction.

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For a given image, its Gabor wavelet transformation is defined as:

1111* ),(),(),( dydxyyxxgyxIyxW mnmn −−= ∫ ∫ (13)

where,* is conjugate.

Mean: dxdyyxwmnmn ∫∫= |),(|μ (14)

Variance:

dxdyyxw mnmnmn

2|)),((|∫∫ −= μσ

(15)

We use mnμ , mnσ as the component structure to

construct the feature vector ]...[ 1,11,10000 −−−−= kmkmf σμσμ

and normalize the feature for the comparison of the similarity.

IV. INTEGRATED COLOR AND TEXTURE

FEATURE FUSION ALGORITHM

The algorithm is as follows: (1) Color Feature Extraction. Use equation (1) ~(3) to construct one-dimensional

feature vector. (2) Normalize the color features. (3) Texture Feature Extraction

Seek the co-occurrence matrix of five characteristics (energy, moment of inertia, entropy, inverse difference), and finally find each feature vector of the mean and standard deviation as the texture feature vector of each component.

Use t transform using equation (11) ~ (13) to extract the image texture features based on Gabor wavelet.

(4) Normalize texture features based on GLCM and Gabor wavelet, respectively.

(5) Normalize color+GLCM features and color+Gabor wavelet features, respectively.

(6) Calculate the integrated similarity This step is to calculate the similarity between the

images using the weighted Euclidean distance. Similarity of the color and texture features are denoted by the 1D

and 2D , then 1 2D D Dα β= + is the integrated

similarity. The weight is determined based on experience.

V. EXPERIMENTS AND ANALYSIS

In this paper, experimental data set contains 1000 images from Corel database of images, divided into 10 categories, each category has 100 images. Experimental images covers a wealthy of content, including landscapes, animals, plants, monuments, transport (cars, planes) and so on. Selection of each type in the 80 images as training samples, 20 samples for testing.

Tablet 1 Two texture features are based on retrieval system performance comparison

Images GLCM

Recall (%)

GLCM

Precision

(%)

Gabor

Wavelet

Recall

(%)

Gabor

Wavelet

Precision

(%)

Ruins 18.9 22.1 23.2 29.5 Planes 48.7 67.6 31.2 39.9

On the images of ruins, using wavelet transform to extract texture effect is relatively good, and for the planes, with the co-occurrence matrix texture features has relatively good retrieval results. After many types of experiments, the statistics can be concluded: a feature extraction method cannot meet all of the images. GLCM is better for the images of which discrimination of the target and background discrimination is relatively large and texture is simple. Gabor wavelet is better for the images of which discrimination of target and background is not large and texture is more complex.

Experimental results show that if we use the texture features based on GLCM , the weight of color and texture are set to 0.5 to thus to ensure the two features has the same influence on the image, if we use the texture features based on Gabor wavelet, the weight of color and texture are set to 0.6 and 0.4, respectively.

Table 2 Performance comparison for some kinds of image retrieval

Retrieval Methods

Mean

Recall(%)

Mean

Precision (%)

HSV Color Space

30.0 46.8

GLCM 33.7 32.3 Gabor

Wavelet 24.7 29.5

Integrated features

39.8 53.9

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The results show that integrated features method is better than a single color-based features or features based on single texture image retrieval.

VI. CONCLUSION

In this paper, we employ different feature extraction and similarity for different texture images. On the one hand, for simple texture features, we integrate HSV color space and GLCM to extract features, using Euclidean distance to emphasize the color features and texture features of equal importance ; on the other hand, for complex features, we integrate HSV color space and Gabor wavelet to extract features and use weighted Euclidean distance, and set the weight of color is greater than the one of Gabor wavelet, emphasizing the color features of image retrieval. Experimental results show that the algorithm improves the accuracy of retrieval. References [1] Song Yan, Liu Fangai. “Image Retrieval Based on

Color and Texture”, Computer Engineering and Design Vol.28, No.17, pp4180-4182, Sep,2007

[2] Finlavson G, Schettine R. Special issue: Color for image indexing and retrieval [ J]. Computer Vision and Image Understanding, 2004, 94(1-3): 1-2.

[3] Qiu Zhaowen, Zhang Tianwen. “A new image color feature extraction method”. Journal of Harbin Institute of Technology, Vol 36, No 12 Dec.,2004

[4] Shang Lin,Yang Yu-Bin,Wang Liang,An Image Texture Retrieval Algorithm Based on Color Co-occurrence Matrix, Journal of Nanjing University (Natural Sciences), Vol.40,No.5,Sept.,2004.

[5] Simona E G,NicolaiPetkov, PeterKruizinga.Comparison oftexture features based on Gabor filters[ J]. IEEE Transac-tions on Image Processing, 2002, 11(10): 1160-1167.

[6] Wei Na,Geng Guohua,Zhou Mingquan.Content-based imagere-trieval using Gabor filters[J].Computer Engineering,2005,31(8): 10-11.

[7] Yang J,IJiu L,Jiang T,ET AL.A modified gaber filter design method for fingerprint image enhancement[J].Pattern Recogni-tion Letters,2003,24:1805-1817.

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