content based image retrieval using combined … based image retrieval using combined features of...

Content Based Image Retrieval using Combined Features of Color and Texture

Features with SVM Classification

R. Usha [1] K. Perumal [2]

Research Scholar [1] Associate Professor [2]

Madurai Kamaraj University, Madurai.

[email protected],

[email protected].

Abstract

Retrieval of an image is a more effective and

efficient for managing extensive image database.

Content Based Image Retrieval (CBIR) is a one of the

image retrieval technique which uses user visual

features of an image such as color, shape, and texture

features etc. It permits the end user to give a query

image in order to retrieve the stored images in

database according to their similarity to the query

image. In this work, content based image retrieval is

accomplished by combining the two features such as

color and texture. Color features are extracted by using

hsv histogram, color correlogram and color moment

values. Texture features are extracted by Segmentation

based Fractal Texture Analysis (SFTA). The combined

features which are made up of 32 histogram values,64

color correlogram values, 6 color moment values and

48 texture features are extracted to both query and

database images. The extracted feature vector of the

query image is compared with extracted feature vectors

of the database images to obtain the similar images.

The main objective this work is classification of image

using SVM algorithm.

Keywords— Image Retrieval; Content based image

retrieval; HSV color histogram; color correlogram; color

moments; SVM Algorithm; Relative Standard Derivation;

Fractal Texture features.

1. Introduction Nowadays in digital photography, to save bulk of

large amounts of high quality images, network speed

and storage capacity has been made possible. Digital

images are used in a wide range of applications such as

geography, medical, architecture, advertising, design,

military and albums. However here we have some

difficulties in searching and organizing the largest

quantity of images in databases. Generally the retrieval

of image is classified into two methods such as

1. Text Based Image Retrieval and

2. Content Based Image Retrieval.

Text Based Image Retrieval is having following

disadvantages such as inefficiency, loss of information,

time consuming process and more expensive task.

These problems are overcome by using Content Based

Image Retrieval for image retrieval. “Content based”

refers that the search will analyse the contents of an

image rather than the data about image such as

keywords, tags, name of file extension like jpg, bmp,

gif etc. Here the „content‟ refers visual informations

such as color, texture and shape that can be derived

from the image itself. Therefore, in this paper we

proposed effective CBIR system using color and

texture feature to overcome these above mentioned

drawbacks of Text based image Retrieval.

2. Related works Image retrieval in CBIR based on the visual

features such as texture, color and shape. In this work

we choose two visual features as texture and color.

Texture analysis, is generally a very time-consuming

process. Research in texture analysis is very important,

because that is used to improve the discriminatory

ability of the extracted image features.

There are three primary issues in texture

analysis, such as texture classification, texture

segmentation and shape recovery from texture. Texture

classification, is a process of identifying the given

texture region from a given set of texture classes.

Texture segmentation is concerned with automatically

determining the boundaries between various textured

regions in an image [1]. In order to accurately capture

the textural characteristics of an image, texture analysis

algorithms use filter banks or co-occurrence gray level

matrices (GLCMs) have to consider multiple

orientations and scales. The computational cost

overhead for applying this method may be heavy. It is

also reported in [2] that SFTA works much faster in

terms of feature extraction time, when compared to

Gabor and Haralick methods.

The main objective of the SFTA is to extract

texture feature in an image which results in the

formation of a feature vector. Haussdorf fractal

dimension method is used in SFTA. To find optimal

threshold Otsu algorithm is used. It is suggested in [3]

R Usha et al , International Journal of Computer Science & Communication Networks,Vol 4(5),169-174

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that fractal dimension can be efficiently computed in

linear time by the box counting algorithm. For real

world images it is suggested in [4], that the Otsu‟s

method provides a better selection of thresholds. In [5],

it is reported that Otsu‟s method the image is assumed

to be composed of only two regions: object and

background, and the best threshold is the one that

maximize the between-classes variance of the two

regions. The Otsu‟s method is also extendable to

multilevel thresholding.

The color of an image is represented from the

famous color spaces like RGB, XYZ, YIQ, L*a*b,

U*V*W, YUV and HSV [6]. It has been reported that

the HSV color space gives the best color histogram

feature, among the different color spaces [7]-[11].In

general, histogram-based retrievals in HSV color space

showed better performance than in RGB color space.

In a viewpoint of computation time and retrieval

effectiveness, using HSV color space is faster than

using RGB color space [12]. Therefore, in this work we

use SFTA texture and HSV color model features are

used for efficient image retrieval to improve the above

mentioned text based image retrieval problems.

3. Methodology Effective image processing techniques are

required to extract visual features such as texture and

color from an image in CBIR system. This system

accepts the input query image from the user. A retrieval

model CBIR performs the image retrieval by

comparing the similarities between the input query

image and database stored images using these extracted

texture and color features. Then the outcome of this

system is to find out relevant image from image

database to query image given by the end user.

The below figure describes the basic

functionality of content based image retrieval. Color

and texture features are extracted to both query image

and database images. Then compare the similarity

between the feature vectors of query and database

image. Precision and recall operation is carried out for

analysis the performance of the system.

Figure 1: Block diagram for Content Based Image Retrieval

4. Feature extraction The feature has been defined as a method of

one or more measurements, each of which identifies

some quantifiable properties of an object, and is

calculated such that it quantifies some significant

characteristics of the object. Feature extraction is a

special form of dimensionality reduction. In this work,

an extraction of features consists of color and texture

digital information. Color and texture features are

extracted by hsv histogram, color correlogram, color

moments and SFTA respectively.

4.1 HSV Color histogram

Color feature is one of the most important things

to access the image. The color of an image is

represented from the famous color spaces like RGB,

XYZ, YIQ, L*a*b, U*V*W, YUV and HSV [1]. HSV

color space gives the best color histogram feature,

among the different color spaces [1]. HSV color space

is represented by three components such as Hue (H),

Saturation(S), and Value (V).

A Color histogram of an input image is

defined as a following vector

H= {H[0], H [1], … H[i], …, H[N]}


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Where i denotes the color bin in the color

histogram. N denotes the total number of bins used in

color histogram and H[i] denotes the total number of

pixel of color I in an image. Generally, every pixel in

an image will be in favour to color histogram bin. So

that, in the image color histogram, each bin value gives

the number of pixels those have the same

corresponding color.

Color histogram should be normalized to

compare the images in various sizes. The normalized

color histogram Hʹ is defined as,

Hʹ=

{Hʹ[0],H

ʹ[1], … …,H

ʹ[i], … ,H

ʹ[n]}

Where Hʹ[i]= H[i] / Total number of pixels in

an image.

Algorithm

The computation of HSV color histogram has

been done by using following steps as,

Step1: Convert RGB color image into HSV color

space.

Step 2: Color quantization is carried out using color

histogram by assigning 8 levels to hue, 2 levels to

saturation and 2 levels to value for give a quantized

HSV space with 8x2x2=32 histogram bins.

Step3: The normalized histogram is obtained by

dividing with the total number of pixels.

4.2 Color correlogram and color moments

Color correlogram gives the information about the

features of colors. It includes spatial color correlations,

which describes the global distribution of local spatial

correlation of colors and is very easy to compute. Color

moment feature is used to differentiate images based on

their color features and it is also gives the similarity of

color measurement between the images. Then the

similarity values are compared with the values of image

indexed at image database for image retrieval.

4.3 Texture feature

Texture features is required here for the below reasons

such as

1. Each class has set of images these color is

independent but these texture

information is dependent from one to one

is shown in Figure 2.

2. Images are having same color with

different texture is shown in Figure 3.

Extraction of texture features may give time

consuming process. Solve this time consuming problem

by implementing SFTA algorithm [2].

Figure 2

Figure3

An enhanced input RGB image is converted into

Grayscale image I. SFTA texture method applied

multilevel Otsu thresholding on Grayscale image I for

decomposing the segmented image in several parts.

This is achieved by selecting pairs of thresholds (lower

threshold tl and upper threshold tu) using Two

Threshold Binary Decomposition (TTBD). SFTA

feature vector correlate with the number of binary

images acquired in TTBD phase. If the standard total

number of extracted threshold is 4, we acquire 8

different binary images. Each binary image has three

feature vectors that depict the boundaries fractal

dimension. The purpose of fractal measurement is used

to narrate the boundaries complexity and segmented

image structures. An extracted vector features are

fractal dimension, mean gray level, and size of area

image. SFTA algorithm has been explained given

below in figure 4.

Require: Grayscale image I and number of threshold

nt.

Ensure: Feature vector VSFTA.

1: T MultiLevelOtsu (I, nt)

2: TA{{ti, ti+1}: ti, ti+1∈ T, i∈ [1..|T|-1]}

3: TB {{ti, nl}: ti∈ T, i∈ [1... ||]}


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4: i0

5: for {{tl, tu}: {tl, tu} ∈ TA ∪ TB} do 6: IbTwoThresholdSegmentation (I ,tl,tu) 7: Δ(x, y)FindBorders (Ib)

8: VSFTA[i]BoxCounting (Δ)

9: VSFTA[i+1]MeanGrayLevel(I, Ib)

10: VSFTA[i+2]PixelCount(Ib)

11: ii+3

12: end for

13: return VSFTA

Figure 4: SFTA Algorithm

The symbol I, Ib, Δ, T, nt, tl, tu and VSFTA

denotes input Grayscale image, binary image, border

image, set of threshold values, and total number of

thresholds, lower threshold, upper threshold and

extracted SFTA feature vectors respectively.

Figure 5: The results of binary image that generate from Two

Thresholding Binary Image. There are 16 images output of a

single input RGB image

5. Similarity comparison

Compare the similarity between the database and

query image by using the relative standard deviation.

The following equation is defined the Relative

Standard Deviation as,

SD= √ 1 ∕ N Σ (Xi-X) 2

RSD=stdev/mean*100

6. Support Vector Machine Algorithm

Support vector machine also known as SVM and is

a supervised machine learning method that examine the

data and identify the patterns, used for classification.

The advantage of this algorithm is to classify the input

query object depends on feature vectors and training

samples.

7. Performance measurement

Generally performance of the CBIR is analysed by

calculating the values of precision and recall values.

Precision:

Precision= Total number of Retrieval Relevant image

Total number of Retrieval image

Recall:

Recall=Total number of Retrieval Relevant image

Total number of relevant image

8. Experimental result

This proposed approach, the image database

contains 400 images. In database images, 100 images

are used for testing and remaining 350 images are used

for training. An input query image and total number of

returned images are desired by the user. Features for

query image are extracted by using SFTA and hsv color

models.


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Then the extracted feature of image database

(Training set) is loaded successfully and it shown

below as,

SVM algorithm classifies the extracted query

image features with relevant features of database

images. Then calculate the recall and precision value

for measuring the performance.

9. Conclusion

CBIR is a process to search the relevant image

in database image when new or query image is given

by the user. In this paper, we use combined color and

texture features. Color features are extracted by using

hsv histogram; color correlogram, color moments and

texture features are extracted by using SFTA.

Combined features are extracted to both query and

database images (training samples).To classify the

query image feature vector with training samples using

SVM algorithm and standard deviation is used here for

similarity measurement. Performance measurement is

calculated by using precision and recall operations.

10. References [1] MihranTuceryan and Anil K.Jain, Texture Analysis,

The Handbook of Pattern Recognition and Computer

Vision, pp.207-248, 1998.

[2] AlceuFerraz Costa, Gabriel Humpire-Mamani,

AgmaJuci Machado Traina, “An Efficient Algorithm

for Fractal Analysis of texture “, Graphics, Patterns and

Images (SIBGRAPI), 2012 25th SIBGRAPI

Conference, pp. 39 -46, 2012.


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[3] C. Traina Jr., A. J. M. Traina, L. Wu, and C.

Faloutsos, “Fast feature selection using fractal

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(SBBD), João Pessoa, Brazil, 2000, pp. 158–171.

[4] P. K. Sahoo, S. Soltani, A. K. C. Wong, and Y.

Chen, “A survey of thresholding techniques,”

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[5] N. Otsu. A threshold selection method from gray-

level histogram. IEEE Trans. Systems Man Cybern.

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[6] T. Gevers, Color in image Database, Intelligent

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Amsterdam, the Netherlands. 1998.

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and quantization for image retrieval”, In SPIE Storage

and Retrieval for Image and Video Databases IV, Vol.

SPIE 2670, pp- 9–16. 1996.

[8] M. W. Ying and Z. HongJiang, “Benchmarking of

image feature for content-based retrieval”, IEEE.Pp-

253-257, 1998.

[9] Z. Zhenhua, L. Wenhui and L. Bo, “An Improving

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[10] E. Mathias, “Comparing the influence of color

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[11] S. Manimala and K. Hemachandran, “Performance

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[12] Sangoh Jeong,” Histogram-Based Color Image

Retrieval”, Psych221/EE362 Project Report

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content based image retrieval using combined … based image retrieval using combined features of...

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