International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 11 - April 2015
Color Based Featural Analysis Using the Content
Based Approach
Divya K V#1, Dr. Suresh .M.B.#2
#1PG Student, #2Prof. and Head, Department of Information Science & Engineering, East West Institute of Technology
Bangalore 560091, Karnataka, India
Abstract—since there is an exponential growth in the
number of applications which use image database
increased the necessity of using Content Based Image
Retrieval (CBIR) over traditional methods. This paper
suggests image retrieval by the use of color analysis which
improves the effectiveness of image database applications
of the image is verified by absorbing color features
fromdifferent color spaces like RGB, HSV and YCbCr.
This paper emphasizes on retrieval of images by color
feature and color spaces using color corelogram which
proves to be the best method as analyzed using precision
and recalls values.
Keywords—Color features,
corelogram, Precision, Recall.
Image
retrieval,
color
I. INTRODUCTION
One of the most important features that make possible the
recognition of images by humans.It isthat property whichrelays
on the reflection of light to the eye and the processing of that
information in the brain. We use color every day to tell the
differences between the specific objects as well as placed.
Usually colors are defined in three dimensional color spaces.
These could either be RGB (Red, Green, and Blue), HSV (Hue,
Saturation, and Value). The last two are dependent on the
human perception of hue , saturated and brightening of images..
Most image formats such as JPEG, BMP, GIF, use the
RGB color space to store information. The RGB color space is
defined as a unit cubing with RGB axel. the vectors with three
co-ordinates represents the color. When all remaining
coordinates are setter to zero the color absorbed is black. When
the co-ordinates is set to 1 the color perceived is white. The
other color spaces operate in a similar fashion but with a
different perception. A digital image may be considered as a
two dimensional array where the array cells correspond to the
image pixels and the values stored in the cells to the values of
color-intensity, in case of a grey scale (single-color) image. A
color image consists of three single-color images that
correspond to the colors RGB.
In this paper we suggest Content Based Image Retrieval
(CBIR) using color analysis. T. Kato [1] defines Content Based
Image Retrieval (CBIR) as a process of retrieving images from
a database based on the features that are relied from the images.
There features presenting the image they are categorized as
General and Domain Specific. General features are lower level
features such as Color, Texture and Shape. Domain Specific are
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higher level features likehuman brain, emotions etc., which are
quiet difficult to extract.
The CBIR gained good amount of popularity due to its
large application base such as crime investigation and
prevention, medical diagnosis, military, photograph archives,
face findings, architectural and engineering designs.The general
steps involved in CBIR are data collection, image preprocessing, feature extraction, classification, and resultant and
retrieved images available to user.
In CBIR, the image that has to be retrieved from the
database needs its features to be compared with the features of
query image which involves two procedures.
Extraction of features: in this process features like color,
texture and shape are extracted such that the featuresextracted
would help in distinguishing features among images .
Matchingof features: here the image features obtained are
matched so as to attain a visual similarity and matching
involves matching features based on distance measures.
Figure 1: Example of CBIR System
II. RELATED WORK
GurpreetKaur, Mnupreetkaur[2], this paper presents a
review of different techniques in content-based image
retrieval. The paper discusses with the fundamental issues of
CBIR. Featuringas Image Retrieval like colors, texturesas well
as shape are classified next. We briefly discuss the similar
measuringrelying on which matchingis made and images are
being retrieved. Dimension reduction as well as indexing
schemes arebeing discussed along with these. For contentbased image retrieval, user interaction with the retrieval
system is crucial since flexible formation and modification of
queries can only be obtained by involving the user in the
retrieval procedure. Finally Relevance feedback is associated
which helps in improving the performance of a CBIR system.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 11 - April 2015
The feature extraction and truly similar image retrieval are
important steps for effecting content-based image retrieval
(CBIR) system. The absorbed features is compressing domain
is attractive area due to the representation of almost all images
in compressed format at present using DCT (Discrete Cosine
Transformation) blocks transformation. During compressing
some of the critical issuesis lost and the perceptual information
is remaining only, which would have signifying energy for
that retrieval in the compressing domain. In this paper, the
color featuring are extracted from the quantized histograms in
the DCT domain using only the DC and the first three AC
coefficients of the DCT blocks of image having more
significant information.The experimental results of the
proposing the approach using the Corel image database show
that the Laplacian filter with the sharpened images give good
performance in retrieval of the JPEG format images as
compared to the median filter in the DCT frequency domain.
Singha, M.; Hemachandran, K.[4] Paul, in this study, an
attempt has been made to study an image retrieval technique
based on the combination of Haar wavelets transformation
using lifting scheme and the color histogram (CH) called
lifting wavelet-based color histogram. The color feature is
described. The Haarwavelets transformation is used to extract
the texture features and the local characteristics of images,
increases the accuracy of the retrieved system. The lifting
scheme reducing the time to retrieve images.The experiment
results indicate that the proposed techniques outperforms the
other schemes, in terms, the average recall and the total
average is being recalled as well.
Yoshitaka, A. Hyoudou, T[5],in the area of retrieving image
databases, one of the promised approaching is to retrieve it by
specifying. However, specifying an example is not always
sufficient to get satisfying results, since one of the image
example does not comprehensive ranges of values that reflect
the various aspects of the retrieval. In this paper, we propose a
method of retrieving images by as usual specifying multiple
image examples that is designed for retrieving sign boards.
Features of color, shape, and of color regions are extracted
from example images, and they are eventually clustering so as
to obtain proper values. Comparing with the QBE systems that
accept only a single image as the queuing condition, MIERS
(Multi-Image Example-based Retrieval System) returns better
retrieval result, where the result showed that specified more
examples helps to improve recall with little deterioration of
precision.
DISADVANTAGES
• Detail analysis of the extracted images were not done, the
images were had very low precision values which was a major
drawback
• Precision of images was not achieved, and was time
consuming to have feature extraction normally done this was a
major disadvantage
• Accurate modeling was not achieved and images were
extremely blur hence enhancement of the images were not
done
• Performance as well as efficiency of the images were
obtained about 60% which did not determine the images which
was not clear in the overall situation
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• That is, the problem of searching for digitized CBIR
usuallyaccepts that the search will analyze the actual contents
of the image.
III. PROPOSED WORK
This proposed system to implement using CBIR using
different features, the proposed system will help producing the
output as images which are relevant to the query image.
Components involved in the proposed architecture are preprocessing,feature extraction, support vector machine.
Pre-processing: the original RGB image is given to this
module.Itremoves unnecessary noise for the proper
representation of data.
Feature extraction: It extracts the color features implements
the color auto correlogram algorithm. This has following steps
Step1: convert RGB to indexed image with 64 colors.
Step2: find correlogram value.
Step3: reshape the value into 4*4*4.
Step4: construct the final correlogram by using distance.
Support vector machine: SVMs are set of related supervised
learning methods used for classifying. They belong to a family
of generalized linear classification. A special property of SVM
is, SVM simultaneously minimize the empirical classification
error and maximize the geometric margin. So SVM is a
support vector machine implements them.
Finally it matches the query image to the most similar images
in the database. Then images are retrieved and displayed the
outcome of the user interaction.
The main challenges of proposed work are it is efficient for
CBIR using Color Feature Analysis. The proposed method has
three main phases for feature extraction. The first phase
obtains Conventional Color Histogram. The second phase
extracts features using the Color Correlogram and the third
Color/Shape-Based Method.
The Conventional Color Histogram
The conventional color histogram (CCH) of an image
indicates the frequency of occurrence of every color in the
image. From a probabilistic perceptiveness it is usually as it
refers to the probability mass functioning of the image
intensities. It captures the intensities of color channels. The
CCH can be represented as
Where A, Band C are the three color
channels and N is the number of pixels in the image
computationally, the construction is based by counting the
number of pixels of each color (in the quantized color space).
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International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 11 - April 2015
images are stored in gray scale images as well, henceforth they
are stored in the database for testing purpose.
Query Image
Figure 2: Proposed system architecture
The ColorCorrelogram
The color correlogram expressing how the spatial correlation
of pairs of colors changes with distance.An image is defined as
a table indexing by color pairs, where the
entry at location
(i, j) is computing by counts number of pixels of color j at a
distance d from a pixel of color i in the image, dividing by „n‟
number of pixels of the obtained image.
The Color-Based Method
A color‐ based method (CBM) in which a quantized color
image I‟ is obtained from the original image I by quantizing
pixel colored of the obtained image. A connected region had
pixels of identified color is regardful of an objects. The area of
each object that usually encodes as the number of pixels in the
object.
Further, the shape in an object is characters by „perimeter
intercepted lengths‟ (PILs), obtained by intercepting the object
perimeter with eight line segments having eight different
orientations and passing through the object center.
IV. EXPERIMENTAL RESULTS
For the purpose of output there are no standard database of
images available therefore we have created our own database
of 200 images considered from own data set of various
sources, such as web, photographs, which are being scanned
and some of them have taken from camera and mobile. For
testing image the outcome of all the images is verified and
displayed.
The above diagram depicts the images that were used to train
the data set and set them in the knowledge base, where in the
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Figure 3: Top 9 retrieved images for RGB colors space based on
similarity distance.
V.CONCLUSION
In this paper images search can be done using content
based, color has been taken as the property for searching,
algorithm is being used, so it has advantages a sort full of the
suitable images will be generated, final result is being
displayed from that sorts order, the efficiency of this system is
also obtained by calculating precession and recall values. The
features is being extracted based on color histogram and
wavelets based techniques, further the classification of the
images is carried out based on minimum distance
classification, the method is found to be 98% accuracy which
is effective and is comparable with other methods in this area.
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