A modern and simplified approach for CONTENT
BASED IMAGE RETRIEVAL
Nitesh Jain
Ojaswi Gupta
Bachelor of Technology
Department of Electronics and Communication
Bhagwan Parshuram Institute of Technology
New Delhi, India
niteshjain54@gmail.com
Bachelor of Technology
Department of Electronics and Communication
Bhagwan Parshuram Institute of Technology
New Delhi, India
ojaswi258@gmail.com
Abstract – This paper presents a simple approach for
CONTENT BASED IMAGE RETRIEVAL. It means that, the
retrieval of image takes place by the content of images
themselves. With the increasing amount of data, Content Based
Image Retrieval has become quite important these days. CBIR is
primarily a part of image processing. It has its application in
different domains like weather forecasting, medical images,
surveillance, remote sensing, criminal record images, etc. To
retrieve the images, the HSV Histogram, Color Moments (CM),
Autocorrelogram Parameters are calculated and then these
values are compared to the values known to the user. These
similarities are compared by Euclidean distance, Manhattan
Distance, Chebyshev Distance and Relative Deviation.
Index Terms – Content Based Image Retrieval (CBIR), Color
Moments (CM), Euclidean Distance, Manhattan Distance, Hue
Saturation Value (HSV).
queries of large image databases based on visual image
content properties such as Example images, or Userconstructed sketches and drawings.
Several online content-based web search engines can also be
mentioned. “WebSEEk” developed by Image and Advanced
Television Lab, Columbia University. It allows making
queries by example and by desired color composition.
Global Memory Net (GMNet) was launched for public access
in late June 2006. It is a digital library of cultural, historical,
and heritage image collections. Different CBIR systems use
different types of user queries. Typically tools for the contentbased image retrieval consist of query statement and a result
presentation; this query can be done by providing an example
image a sketch, or by choosing desired colors for the image.
Results are presented by the top several similar images based
on the similarity measure.
I. INTRODUCTION
Content-based Image Retrieval (CBIR) is a method used
for retrieving the similar images to a given query image from a
database. It has many applications in the field of medical :
used to diagnose the disease by comparing the various
diagnostic information present initially , weather forecasting :
to calculate whether information by comparing past history ,
survelinace : to know the thief is same or different , Crime
prevention – it helps police in suspicious people’s
identification from large image databases and Military : for
detecting enemy soldiers.
.
Fig. 1 Basic CBIR Working
II. LITERATURE SURVEY
Many CBIR systems and tools have been developed to make
queries based on visual content. IBM developed Query By
Image Content (QBIC) system, which lets user to make
III. BASIC CONTENT AND PROPOSED WORK
Typical CBIR system has two main functionalities. This is
Data insertion and query processing.Data insertion procedures
are performed independent of user interaction. They are
applied to all the data. The purpose of this process is to extract
visual features from the images in the database. These features
are obviously smaller than the actual image and they are then
stored for easy comparison reasons, as a characterizers of each
image.
Query processing starts with user specific request. Request can
be done in several ways: By an example image, by giving
desired pattern or object, colour distribution and etc. Query
processing module obtains the visual features from the given
request, metric is defined.
Features extraction itself involves, selecting the features that
have to be extracted, it depends on the type of user query. The
feature extracting algorithm is chosen to create the feature
vector from the selected features.
A. Colour Comparison Technique
Image content comparison by colour is based on matching
images by their colour distribution. In this case image feature
identifies the proportion of pixels of specific colour or colours
within an image. So one can make colour searches by
indicating desired concentration of colours or by an example
image with desired colour distribution and get similar images.
Colour histograms are widely used to extract the colour
distribution descriptors from the image. It is a statistic of the
colour of pixels in the image. First colour distribution is
represented by appropriate colour histogram, and then colour
vector is formed from that histogram.
by a new metric in which the distance between two points is
the sum of the absolute differences of their Cartesian
coordinates. The taxicab metric is also known as rectilinear
distance,
L1
distance,
snake distance, city
block
distance, Manhattan distance or Manhattan length, with
corresponding variations in the name of the geometry.
B. Texture
E. L2 Distance Similarity Extraction
Retrieval by image texture in a similar to colour-based feature
extraction, but it looks for visual patterns in images rather than
colours. So it looks at homogeneity that is not a result of a
single colour presence or intensity of a pixel value. Sometimes
it also provides more spatial information.
The most basic method used to extract the texture descriptor
from the image is based on Fourier Transform. The initial
image is transformed by the Fourier function. As the method
works on digital images, Discrete Fourier Transform (DFT) is
used. DFT converts images from the spatial domain into the
frequency domain, where all the spatial frequencies of the
original image are represented. In another words this
transformed image shows intensity variations over a number
of pixels. Transformed data is grouped to obtain several
measures from it.
In mathematics, the Euclidean distance or Euclidean metric is
the "ordinary" straight-line distance between two points
in Euclidean space. With this distance, Euclidean space
becomes a metric space. The associated norm is called
the Euclidean norm. A generalized term for the Euclidean
norm is the L2 norm or L2 distance.
C. RGB to HSV Feature Extraction
IV. EXPERIMENTAL RESULTS
James S. Wang et al. has provided a database which is known
as wang database. This wang database is used to test the
proposed method. This database cons ist 1,000 images of 10
classes. Each class has 100 images. These 10 classes are
composed of African people, sea, building, dinosaur, bus,
elephant, flower, horse, mountain, and food.
A. Image 1
We want to convert the image to HSV because working with
HSV values is much easier to isolate colors. In the HSV
representation of color, hue determines the color you want,
saturation determines how intense the color is and value
determines the lightness of the image. As can be seen in the
image below, 0 on the wheel would specify a mild red color
and 240 would specify a blue color. In MATLAB, the hue
ranges from 0 to 1 instead of 0 to 360.
Similarity : L2
Retrieved Images : 20
Correct Output : 100%
B. Image 2
Fig. 2 HSV Representation of a particular color
D. L1 Distance Similarity Extraction
A taxicab geometry is a form of geometry in which the usual
distance function or metric of Euclidean geometry is replaced
Similarity : L1
Retrieved Images : 20
Correct Output : 90%
C. Image 3
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Similarity : Relative Deviation
Retrieved Images : 15
Correct Output : 86.67%
[6]
V. CONCLUSIONS AND FUTURE SCOPE
This paper reviewed the main components of a content based
image retrieval system,
including image feature
representation, indexing, query processing, and query-image
matching and user's interaction, while highlighting the current
state of the art and the key-challenges.
A challenging task of development, implementation and
integration of various novel algorithms to result into GUI
based, selectable multi-modal processing of selectable single
query image for retrieval of similar images has been achieved
successfully.
These algorithms include:



Edges and prominent boundaries detection
Foreground separation
Image retrieval based on
o Colour codes of entire image
o Foreground colour codes
o Foreground shape correlation
o Combination of foreground colour codes
and shape correlation
Suggested future enhancements are as under.


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Analysis of performance of prominent boundaries
detection method with other wavelets.
Utilization of well localized thin-edges to further
reduce artifacts produced due to intrinsic
characteristic of watershed algorithm.
Incorporation of indexing technique(s) for faster
query response.
Incorporation of database management modules for
image and image feature databases.
Incorporation of relevance feed-back from user to
increase the retrieval performance of the system.
Incorporation of multiple-queries to refine results for
improved retrieval performance.
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