International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 8, August 2013)
Fast Retrieval of Images Using Filtered HSV Color Level
Detection
Durgesh Nirapure, Prof. Udaypal Reddy
Department of Computer Science Engineering, IES College of Technology Bhopal, India
Models of Color Levels for Feature Extraction
The extraction of the color features for each of the four
methods is performed in the HSV (hue, saturation and
value) which is converted from RGB color space hsv, to
perceptual color space, where Euclidean distance
corresponds to the human visual system’s notion of
distance or similarity between colors.
Abstract-- Image searching is the wide area of research in
the field of data mining and in web mining also. This work
proposes the method which efficiently searches the images
from database with high speed as well as high performance.
The major goal of retrieval of images from a large database is
the process of searching images based on color levels in HSV
color space. The HSV color space based retrieval of images
has wide area of application in the field of data mining. In this
work the method works on the higher color level in the
database and retrieves the most appropriate results in
descending order. The aim of this work is to explore efficient
way based on color to search images in database with
considerable speed.
(a) Color Level Histogram
The conventional color histogram (CCH) of an image
indicates the frequency of occurrence of every color in the
image. From a probabilistic perspective, it refers to the
probability mass function of the image intensities. It
captures the joint probabilities of the intensities of the color
channels.
The CCH can be represented as
Keywords-- Image Retrieval, HSV Color space.
I. INTRODUCTION
The evolution of technology has become the wonder of
universe and this leads the faster and more efficiently
exchange of information in the form of images in parallel
the advancement with the large storage technology the
collection of images has the fashion and facilitates the wide
areas to execute the applications. As the storage is large
there is need to fetch images from large collection for our
concern only and this is the point where we need the data
mining algorithms to perform such things. Consequently,
the search for relevant information in the large storage of
image databases has become more challenging. The main
challenge lies in the advancement of retrieval speed with
considerable accuracy. How to achieve accurate retrieval
results are still a challenging and an unsolved research
problem. A typical image retrieval system includes three
major components: feature extraction, high dimensional
indexing and system design [2]. In this work, we study the
first component; that of low-level feature extraction, and
we attempt to answer the following question: What are the
color features that need to be extracted from an image, in
order to achieve the highest retrieval performance, at a
relatively low computational speed? The main contribution
of this work is a comprehensive comparison of color
feature extraction approaches.
h A,B,C(a,b,c) = N. Prob(A=a, B=b, C=c)
Where A, B and C are the three color channels and N is
the number of pixels in the image [3].
Computationally, it is constructed by counting the
number of pixels of each color (in the quantized color
space).
(b) Color histogram based on Fuzzy Logic
In the fuzzy color histogram (FCH) approach, a pixel
belongs to all histogram bins with different degrees of
membership to each bin. More formally, given a color
space with K color bins, the FCH of an image I is defined
as F(I)=[f1,f2,…fk] where
Where N is the number of pixels in the image and µij is
the membership value of the jth pixel to the ith color bin,
and it is given by µij = 1/(1 + dij/ ς ), where dij
is the Euclidean distance between the color of pixel j(a 3‐
dimensional vector of the H, S and V components),
and the ith color bin, and ς is the average distance
between the colors in the quantized color space.
414
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 8, August 2013)
(c) The Color Correlogram
The color correlogram (CC) expresses how the spatial
correlation of pairs of colors changes with distance. A CC
for an image is defined as a table indexed by color pairs,
where the dth entry at location (i,j) is computed by counting
number of pixels of color j at a distance d from a pixel of
color i in the image, divided by the total number of pixels
in the image.
its colour and texture provides a better result than using a
single colour feature since two features are now used as
indicator during matching process.
In this research we focus on the method and strategies to
retrieve images by using both colour and texture feature
vectors to produce more accurate results.
III. SYSTEM MODEL
Color feature of HSV
We evaluate the content based image retrieval HSV
color space of the images in the database. The HSV
stands for the Hue, Saturation and Value, provides the
perception representation according with human visual
feature. The HSV model, defines a color space in terms
of three constituent components: Hue, the color type Range
from 0 to 360. Saturation, the "vibrancy" of the color:
Ranges from 0 to 100%, and occasionally is called the
"purity". Value, the brightness of the color: Ranges from
0 to 100%. HSV is cylindrical geometries, with hue, their
angular dimension, starting at the red primary at 0°,
passing through the green primary at 120° and the blue
primary at 240°, and then back to red at 360° [8, 9]. The
HSV planes are shown as Figure 1.
(d) Color Indexing
In color indexing, given a query image, the goal is to
retrieve all the images whose color compositions are
similar to the color composition of the query image.
Typically, the color content is characterized by color
histograms, which are compared using the histogram
intersection distance measure[3].
Content-based image retrieval system attempts a tradeoff between the two techniques and combines the features
of both. The attributes commonly used in CBIR are colour,
texture, shape and motion of which shape is the key
attribute. The images are searched from a multimedia
database by searching the content of the stored images and
retrieving images similar to the input query image. The
database can be considered as a cluster of databases and
each cluster having maximum similarity between images
and least inter-cluster similarity [7]. It proposes to search
within each cluster for the query image. The relevance
feedback strategy is used in displaying search results. The
results are usually returned to the user in the form of
multimedia presentation as proposed in Ref [6]. The
relevance feedback strategy refines the output according to
the similarity with the query image. In a typical CBIR
system object boundaries are searched for the query image.
This can be done using active contour model with which
one can easily detect an object boundary.
II. CURRENT IMAGE RETRIEVAL APPROACHES
CBIR is basically a two step process which is Feature
Extraction and Image Matching (also known as feature
matching). Feature Extraction is the process to extract
image features to a distinguishable extent. Information
extracted from images such as colour, texture and shape are
known as feature vectors. The extraction process is done on
both query images and images in the database. Image
matching involves using the features of both images and
comparing them to search for similar features of the images
in the database [5]. Using multiple feature vectors to
describe an image during retrieval process increases the
accuracy when compared to the retrieval using single
feature vector. For example, searching of image based on
Figure 1. The Different planes of HSV color space
The quantization of the number of colors into several
bins is done in order to decrease the number of colors used
in image retrieval, J.R. Smith [5] designs the scheme to
quantize the color space into 166 colors. Li [12] design
the non-uniform scheme to quantize into 72 colors. We
propose the scheme to produce 15 non-uniform colors.
415
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 8, August 2013)
The formula that transfers from RGB to HSV is defined as
below:
not complete image than prepare another database which
contains features only.
Than doing comparison with the query image makes
comparison faster. After that similarity matrix preparation
order it with descending similarity, fetch indices of the
most similar records and then display the retrieved images.
The flow of algorithm is explained in the flow chart, it also
explain the how the one test process or retrieval of images
from large database is done. The below flow chart showing
the steps to search images in figure 3:
Start
The R, G, B represent red, green and blue components
respectively with value between 0-255. In order to obtain
the value of H from 0o to 360 o, the value of S and V
from 0 to 1, we do execute the following formula:
Remove noise from RGB images and
calculate HSV values
H= ((H/255*360) mod 360
V= V/255
S= S/255
Prepare Database of HSV values of
Images
Proposed Approach:
Find Levels of Hue, Saturation and Value
in image and normalize it
Choose test image to search from similar
images from database and preprocess it
Compare with database records
Fig.2 Block diagram of proposed methodology
The proposed approach to retrieve the images from
database using color level is the method which is based on
the color level present search of things which has the
practical aspects in many of the day to day applications in
the security, traffic, surveillance as well as in the data
mining or in web mining of images needed. The best part
of this proposed approach to extract color features from the
images of the database and then search accordingly. That
gives us the way faster classification of images from large
stake.
The block diagram of proposed methodology is showing
the major blocks of proposed system. The first block is the
input query image i.e. test image we want to search similar
images from databases. Than preprocess the test image to
compare with the database images that is previously
processed. The comparison from database with lots of
images takes time so there is a need which reduces the size
of database to compare so the preprocessing of database is
the actual process of extracting features only from images
Arrange similar results in descending
order
Display results
End
Figure 3. Flow chart of Proposed System
IV. S IMULATION RESULTS
The simulation a result of the proposed system is given
below the whole simulation process is completed in the
following experiments. The experiments work as explained
in the flow chart of proposed approach. In the results first
image is the query image and the next one is the retrieved
results.
416
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 8, August 2013)
Experiment 1:
In this experiment we have taken query image which
contains green color of grass gray color of water, and blue
color of sky than the algorithm searches for these color
levels in the database and gives results in the most similar
images.
Figure 4. Query Image
Fig. 7. Searched Results of experiment 2
In this experiment we have taken query image which
contains tulip flower of red and yellowish green color its
root and leaf and black colored background. Than the
algorithm searches for these color levels in the database
and gives results in the most similar images.
The experiment performed on the query images with
different color combinations and their recall and precision
values are given below in the table.
Table 1.1
Precision-Recall Curve for the Query Images
SNo.
Figure 5. Searched Results of experiment 1
Experiment 2:
Figure 6 Query image
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Query Images
Precision
Recall
69.58%
99.49%
1
'1 dam.jpg'
2
'2 Red Flower.jpg'
0.09%
0.17%
3
'3 lake.jpg'
33.01%
94.84%
4
'4 nature.jpg'
32.33%
79.06%
5
'5 red Flower.jpg'
25.79%
83.57%
6
'6 Tulip.jpg'
14.15%
45.05%
7
'7 sun flower.jpg'
11.08%
16.94%
8
'8 Blue Flower.jpg'
5.31%
59.18%
9
'9 Water.jpg'
41.85%
95.08%
10
'10 Tawa Restaurent.jpg'
38.08%
76.72%
11
'11 tawa resort.jpg'
19.35%
87.67%
International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 8, August 2013)
After performing the retrieval process on more different
query images we get the the values of recall and precision
for each experiment and after plotting these values we get
recall-precision curve of our system which is given below.
Precision Recall Curve of Proposed Methodology
[4]
Ying Liu, Dengsheng Zhang, Guojun Lu, Wei-Ying Ma, “A survey
of content-based image retrieval with high-level semantics”,
Pattern Recognition, Volume: 40, Issue: 1, pp. 262-282, January,
2007.
[5]
J R Smith, “Integrated spatial and feature image system:
Retrieval, analysis and compression “[Ph D dissertation], Columbia
University, New York, 1997.
[6]
Jain, A & Vailaya,A ,(1996) ” Image retrieval using colour and
shape”, Pattern Recognition, Vol. 29, pp1233-1244.
[7]
M. S. Lew, N. Sebe, C. Djeraba, and et al,“Content based
multimedia information retrieval: State of the art and challenges”,
ACM Trans. Multimedia Comput. Commun. Appl., Vol.2, No. 1, 119, 2006.
[8]
D. Feng, W. C. Siu, and H. J. Zhang, “Fundamentals of
Content-Based Image Retrieval, in Multimedia Information
Retrieval and Management Technological Fundamentals and
Applications.”New York: Springer, 2003.
[9]
Nezamabadi-pour, H. & Kabir, E., (2004) “Image retrieval
using histograms of uni-colour and bicolour blocks and directional
changes in intensity gradient”, Pattern Recognition Letters, Vol.
25, pp1547- 1557.
1
0.9
0.8
0.7
Precision
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
Recall
0.6
0.7
0.8
0.9
1
Fig.8. Precision-Recall Curve
[10] Vadivel A. ,Majumdar A. K. & Sural Shamik, (2003)
"Perceptually Smooth Histogram Generation from the HSV Colour
Space for Content Based Image Retrieval", International Conference
on Advances in Pattern Recognition, Kolkata, pp 248-251.
V. CONCLUSIONS A ND FUTURE SCOPE
The proposed methodology of image retrieval in this
paper is fast but there is always a provision of
improvements in the existing system. The current algorithm
works on HSV color model of images for the retrieval from
database, but there are other color models are also available
which may be efficiently works for retrieval applications.
The future of this method will be the improvement in speed
of retrieval as well as precision in results. The color based
retrieval has wide area of implementation in the field of
data mining and web mining where color is the important
classification criteria.
[11] J R Smith, “Integrated spatial and feature image system:
Retrieval, analysis and compression “[Ph D dissertation], Columbia
University,New York, 1997.
[12] Li, Liu and Cao, “An Image Retrieval Method Based on Color
Perceived Feature”, Journal of Image and Graphics, 1999.
[13] Jun Tang, “A Color Image Segmentation algorithm Based on Region
Growing” 2010.
[14] Yoichi MIYAKE and Kimiyoshi MIYATA “Color Image Processing
Based on Spectral Information and Its Application” 1999.
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International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 8, August 2013)
[21] Weisheng Li “New Color Cluster Algorithm For Image Retrieval”
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AUTHOR’S PROFILE
Durgesh Nirapure is research scholar and
pursuing his Master of Technology
Computer Science Engineering from IES
College of Technology Bhopal, India. He is
very keen to study the image processing
techniques and its retrieval processes.
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