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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.
usha.resch@gmail.com,
perumalmala@gmail.com.
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
169
ISSN:2249-5789
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]}
R Usha et al , International Journal of Computer Science & Communication Networks,Vol 4(5),169-174
170
ISSN:2249-5789
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... ||]}
R Usha et al , International Journal of Computer Science & Communication Networks,Vol 4(5),169-174
171
ISSN:2249-5789
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.
R Usha et al , International Journal of Computer Science & Communication Networks,Vol 4(5),169-174
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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.
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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.
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[6] T. Gevers, Color in image Database, Intelligent
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[8] M. W. Ying and Z. HongJiang, “Benchmarking of
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[9] Z. Zhenhua, L. Wenhui and L. Bo, “An Improving
Technique of Color Histogram in Segmentationbased
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384, 2009.
[10] E. Mathias, “Comparing the influence of color
spaces and metrics in content-based image
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[11] S. Manimala and K. Hemachandran, “Performance
analysis of Color Spaces in Image Retrieval”, Assam
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ISSN:2249-5789
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