Et35839844

Mohd. Danish et al. Int. Journal Of Engineering Research And Application
ISSN : 2248-9622, Vol. 3, Issue 5, Sep-Oct 2013, Pp.839-844

RESEARCH ARTICLE

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OPEN ACCESS

A Survey: Content Based Image Retrieval Based On Color,
Texture, Shape & Neuro Fuzzy
Mohd. Danish*, Ritika Rawat **, Ratika Sharma ***
*(Department of CSE A.I.M.T Greater Noida, India)
**(Department of CSE A.I.M.T Greater Noida, India)
***(Department of CSE A.I.M.T Greater Noida, India)

Abstract
In current technology the acquisition, transmission, storing, and manipulation are allowed on the large
collections of images. With the increase in popularity of the network and development of multimedia
technologies, users are not satisfied with the traditional information retrieval techniques. So nowadays, the
content based image retrieval is becoming a source of exact and fast retrieval. Content Based Image Retrieval
(CBIR) is a technique which uses visual features of image such as color, shape, texture, etc. to search user
required image from large image database according to user's requests in the form of a query image. Images are
retrieved on the basis of similarity in features where features of the query specification are compared with
features from the image database to determine which images match similarly with given features. Feature
extraction is a crucial part for any of such retrieval systems. So far, the only way of searching these collections
was based on keyword indexing, or simply by browsing. Literature survey is most important for understanding
and gaining much more knowledge about specific area of a subject. In this paper we survey some technical
aspects of current content-based image retrieval systems and described the image segmentation in image
processing and the features like neuro fuzzy technique, color histogram, texture, and shape for accurate and
effective Content Based Image Retrieval System after doing the deep study of related works.
Keywords— Content-Based Image Retrieval (CBIR), Neuro Fuzzy, Color Histogram, Texture, HSV Color and
Wavelet decomposition, Image Segmentation.

I.

INTRODUCTION

With the development of the Internet, and
the availability of image capturing devices such as
digital cameras, image scanners, the size of digital
image collection is increasing rapidly. Efficient
image searching, browsing and retrieval tools are
required by users from various domains, including
remote sensing, fashion, crime prevention,
publishing, medicine, architecture, etc. For this
purpose, many general purpose image retrieval
systems have been developed. There are two
frameworks: text-based and content-based. The textbased approach can be tracked back to 1970s. In such
systems, the images are manually annotated by text
descriptors, which are then used by a database
management system. (DBMS) to perform image
retrieval. There are two disadvantages with this
approach. The ﬁrst is that a human labour at
considerable level is required for manual annotation.
The second is the inaccuracy in annotation due to the
subjectivity of human perception. To overcome these
disadvantages in text-based retrieval system, contentbased image retrieval (CBIR) was introduced in the
early 1980s. In CBIR, images are indexed by their
visual content, such as color, texture, shapes. [1]The
fundamental difference between content-based and
text-based retrieval systems is that the human
interaction is an essential part of the latter system.
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1.1
i)

Classification of Images
Intensity Images
It represents an image as a matrix where
every element has a value corresponding to how
bright/dark the pixel at the corresponding position
should be colored. There are two ways to represent
the number that represents the brightness of the pixel:
The double class (or data type). This assigns a
floating number ("a number with decimals") between
0 and 1 to each pixel. The other class is called uint8
which assigns an integer between 0 and 255 to
represent the brightness of a pixel. [2, 3]
ii)

Indexed Images
In an indexed image, the image matrix
values do not determine the pixel colors directly.
Instead, MATLAB uses the matrix values as indices
for looking up colors in the figure's colormap. This is
a practical way of representing color images. An
indexed image stores an image as two matrices. The
first matrix has the same size as the image and one
number for each pixel. The second matrix is called
the color map and its size may be different from the
image. [2, 3]
iii)

Scaled indexed images
A scaled indexed image uses matrix values.
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The difference is that the matrix values are linearly
scaled to form lookup table indices. To display a
matrix as a scaled indexed image, use the MATLAB
image display function imagesc. [2, 3]
iv)

Binary Images
This image format also stores an image as a
matrix but can only color a pixel black or white (and
nothing in between). It assigns a 0 for black and a 1
for white. [2, 3]
1.2

Content Based Image Retrieval
The term Content-based image retrieval was
originated in 1992, when it was used by T. Kato to
describe experiments into automatic retrieval of
images from a database, based on the colors and
shapes present. Since then, this term has been used to
describe the process of retrieving desired images
from a large collection on the basis of syntactical
image features. The techniques, tools and algorithms
that are used originate from fields such as statistics,
pattern recognition, signal processing, and computer
vision. [4]
In content-based image retrieval (CBIR), the image
databases are indexed with descriptors derived from
the visual content of the images. Most of the CBIR
systems are concerned with approximate queries
where the aim is to ﬁnd images visually similar to a
speciﬁed target image. In most cases the aim of CBIR
systems is to replicate human perception of image
similarity as well as possible. [5]
The process of CBIR consists of the following stages:
(1) Image acquisition: to acquire a digital image.

Image Database: It consists of the collection
of n number of images depends on the user range and
choice.
(2) Image preprocessing: to improve the image in
ways that increases the chances for success of the
other processes. The image is first processed in order
to extract the features, which describe its contents.
The processing involves filtering, normalization,
segmentation, and object identification. Like, image
segmentation is the process of dividing an image into
multiple parts. The output of this stage is a set of
significant regions and objects.
(3) Feature Extraction: Features such as shape,
texture, color, etc. are used to describe the content of
the image. The features further can be classified as
low-level and high-level features. In this step visual
information is extracts from the image and saves
them as features vectors in a features database .For
each pixel, the image description is found in the form
of feature value (or a set of value called a feature
vector) by using the feature extraction .These feature
vectors are used to compare the query with the other
images and retrieval.
(4) Similarity Matching: The information about each
image is stored in its feature vectors for computation
process and these feature vectors are matched with
the feature vectors of query image (the image to be
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search in the image database whether the same image
is present or not or how many are similar kind images
are exist or not) which helps in measuring the
similarity. This step involves the matching of the
above stated features to yield a result that is visually
similar with the use of similarity measure method
called as Distance method. Here is different distances
method available such as Euclidean distance, City
Block Distance, Canberra Distance.
(5) Resultant Retrieved images: It searches the
previously maintained information to find the
matched images from database. The output will be
the similar images having same or very closest
features as that of the query image. [6]
(6)User interface and feedback which governs the
display of the outcomes, their ranking, the type of
user interaction with possibility of refining the search
through some automatic or manual preferences
scheme etc.[7]
Relevance
Feedback
User
Query
Formatio
n

Visual
Content
Description

Feature
Vectors

Image
Database

Visual
Content
Description

Feature
Database

Similarity
Comparison

Indexing &
Retrieval
Output
Retrieval Result

Fig 1.1 CBIR System and its various components
1.3
Application of CBIR systems
(1) The advantages of such systems range from
simple users searching a particular image on the
web.
(2) Various types of professionals like police force
for picture recognition in crime prevention.
(3) Medicine diagnosis
(4) Architectural and engineering design
(5) Fashion and publishing
(6) Geographical information and remote sensing
systems
(7) Home entertainment [8]
1.4

Main Challenges to CBIR systems
There could be many challenges faced by a
CBIR system such as:
(1)The issue related to the Semantic gap where it
means the lack of coincidence between the
information that one can extract from the visual data
and the interpretation that the same data have for a
user in a given situation. The user wants to seek
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semantic similarity, but the database can only provide
similarity by data processing. [9]
(2) The expectation of users for huge amount of
objects to search among. [8]
(3)Sometimes incompleteness of query specification
seems to be a challenge.
(4)Incomplete image description is also a source of
challenge to an efficient CBIR system.

increasing towards the top, as in fig.4.2 [11]

1.5
Kinds of CBIR
i) General: In this type of model we try to match a
query image to an arbitrary collection of images.
ii) Application specific: Here we try to match a
query image to a collection of images of a specific
type (e.g. Finger prints, X-ray images of specific
organs). [9]

Fig.1.3 The RGB color model mapped to a cube. The
origin, black, is hidden behind the cube. [11]

1.6

Feature Extraction
Feature extraction is the basis of content
based image retrieval. Typically two types of visual
feature in CBIR:
(1) Primitive features which include color, texture
and shape.
(2) Domain specific which are application specific
and may include, for example human faces and finger
prints. [7]
Color
Color is one of the most widely used lowlevel visual features and is invariant to image size
and orientation.

ii)

Texture
A texture measure is also a larger variety
feature used for image retrieval. Some of the most
common measures for capturing the texture of images
are wavelets and Gabor filters. These texture
measures try to retrieve the image or image parts
characteristics with reference to the changes in
certain directions and the scale of the images. This is
most useful for region or images with homogeneous
texture. [9]

i)



Color Histogram
One of the most popular features used in
CBIR is the color histogram in the HSV color space,
as used in MPEG-7 descriptor .The images are firstly
converted to the HSV color space, and a 64-bin color
histogram is generated by uniformly quantizing H, S,
and V components into 16, 2, and 2 regions,
respectively. [11]

Fig. 1.2 The HSV space color [11]


Color Moments
The mean μ, standard deviation σ, and skew
g are extracted from the R, G, and B color spaces to
form a 9-dimensional feature vector 1[10].
RGB is the best known space color and is it
commonly used for visualization. The acronym
stands for Red Green Blue. This space color can be
seen as a cube where the horizontal x-axis as red
values increasing to the left, y-axis as blue increasing
to the lower right and the vertical z-axis as green
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Fig.1.4 Example of textures [11]
iii)

Shape
Shape may be defined as the characteristic
surface configuration of an object; an outline or
contour. It permits an
object to be distinguished from its surroundings by its
outline. Shape representations can be generally
divided into two categories:
(1)Boundary-based--- shape representation only uses
the outer boundary of the shape. This is done by
describing the considered region using its external
characteristics; i.e., the pixels along the object
boundary. [12]
(2)Region-based--- shape representation uses the
entire shape region by describing the considered
region using its internal characteristics; i.e., the pixels
contained in that region.[12]
Methods used for edge detection as preprocessing
are:

Canny edge detection algorithm

Sobel edge detection algorithm


The Canny edge detection algorithm:
The Canny edge detection algorithm is
popular as the optimal edge detector.
Here, an "optimal" edge detector means:
Good detection – the algorithm should mark as
many real edges in the image as possible.
Good localization – edges marked should be as
close as possible to the edge in the real image.

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
Minimal response – a given edge in the
image should only be marked once, and where
possible, image noise should not create false edges.
[12]


Sobel Edge Detection Algorithm:
As designs become larger and more
complicated, it has become necessary to describe a
design at a high level. This high level description not
only enables the designer to run simulations faster, it
can also be used throughout the development process
for verification. This resulting process allows
developers to identify bugs early on and avoid costly
bug discovery towards the end of development. This
high-level design is usually done by system
engineers. [12]
iv)

The retrieval based on Neuro Fuzzy
The technique of the proposed neurofuzzy
content based image retrieval system in two stages.
Stage 1: the query to retrieve the images from
database is prepared in terms of natural language
such as mostly content, many content and few
content of some specific color. Fuzzy logic is used to
define the query. [13]
Image
Database

Query

Fuzzy
Interpretation

Feature
Database

Neural
Network

Confidential
Factor

Fig.1.5 Block diagram of Neuro Fuzzy system [13]
1.7

CBIR with relevance feedback
A major task in the CBIR systems is the
similarity matching between the query image and the
retrieved images. Unfortunately, the gap between
high-level concepts and low-level features, as well as
the subjective perception for the visual content by the
human beings, result a significant mismatch between
the retrieval results judged manually and by the
computers. To improve the retrieval precision, human
interactions are usually involved. Relevance feedback
is an interactive process which integrates ‘users’
evaluation of the retrieval results. Typically, the
relevance feedback technique includes an interactive
scoring system to evaluate the past retrieval results to
improve the subsequent content retrieval. Various
techniques, such as Radial Basis [10]
1.8

Wavelet Transformation
Wavelet analysis is an exciting new method for
solving difficult problems in mathematics, physics,
and engineering, with modern applications as diverse
as wave propagation, data compression, signal
processing, image processing, pattern recognition,
computer graphics, the detection of aircraft and
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submarines and other medical image technology. The
wavelet representation of a function is a new
technique. Wavelet transform of a function is the
improved version of Fourier transform. [14]

II.

LITERATURE REVIEW

2.1 Existing Image Retrieval Systems
Since the early 1990s, content-based image
retrieval has become a very active research area. Both
commercial and research image retrieval systems,
have been built. Most image retrieval systems
support one or more of the following options [15]:
 random browsing
 search by example
 search by sketch
 search by text (including key word or speech)
 navigation with customized image categories.
Today, there is the provision of a rich set of
search options, but in practical applications which
involves actual users still need systematic studies to
explore the trade-offs among the different options
mentioned above. Here, we will select a few
representative systems and highlight their distinct
characteristics. [15]
Some of the existing CBIR systems [15] are:
 QBIC or Query by Image Content It is the
first commercial content based retrieval
system. This system allows users to
graphically pose and refine queries based on
multiple visual properties such as color,
texture and shape. It supports queries based
on input images, user-constructed sketches,
and selected colour and texture patterns.
 VisualSEEK and WebSEEK VisualSEEk is
a visual feature search engine and WebSEEk
is a World Wide Web oriented text/image
search engine, both of which are developed
at Columbia University.
 Virage Virage is content based image search
engine developed at Virage Inc.It supports
color and spatial location matching as well
as texture matching.
 NeTra This system uses color, shape, spatial
layout and texture matching, as well as
image segmentation.
 MARS or Multimedia Analysis and
Retrieval System This system makes use of
colour, spatial layout, texture and shape
matching.
 Viper or Visual Information Processing for
Enhanced Retrieval .This system retrieves
images based on color and texture matching.
 The img (Anaktisi) The img (Anaktisi) is a
CBIR system on the web based on various
descriptors which includes powerful color
and texture features. The img (Anaktisi)
provides different ways to search and
retrieve them.

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2.2 Related Work
Jaiswal, Kaul [8] concluded that content
based image retrieval is not a replacement of, but
rather a complementary component to text based
image retrieval. Only the integration of the two can
result in satisfactory retrieval performance. In this
paper they reviewed the main components of a
content based image retrieval system, including
image feature representation, indexing, and system
design, while highlighting the past and current
technical achievement.
Ivan Lee, et.al. (1996) [10] have present the analysis
of the CBIR system with the human controlled and
the machine controlled relevance feedback, over
different network topologies including centralized,
clustered, and distributed content search. In their
experiment for the interactive relevance feedback
using RBF, they observe a higher retrieval precision
by introducing the semi-supervision to the non-linear
Gaussian-shaped RBF relevance feedback.
Verma, Mahajan, (2012) [13] have used canny and
sobel edge detection algorithm for extracting the
shape features for the images. After extracting the
shape feature, the classified images are indexed and
labeled for making easy for applying retrieval
algorithm in order to retrieve the relevant images
from the database. In their work, retrieval of the
images from the huge image database as required by
the user can get perfectly by using canny edge
detection technique according to results.
Ryszard S. Chora´s (2007) [16] contributes their
work for the identification of the problems existing in
CBIR and Biometrics systems describing image
content and image feature extraction. They have
described a possible approach to mapping image
content onto low-level features. Their paper
investigated the use of a number of different color ,
texture and shape features for image retrieval in
CBIR and Biometrics systems.
Pattanaik , Bhalke (2012) [17] has worked to prove
that Content Based Image Retrieval has overcome all
the limitation of Text Based Image Retrieval by
considering the contents or features of image. A
query image can be retrieved efficiently from a large
database. A Database consists of different types of
images has implemented on the system. Different
Features such as histogram, color mean, Color
structure descriptor texture is taken into consideration
for extracting similar images from the database. From
the experimental result it is seen that combined
features can give better performance than the single
feature. So selection of feature is one of the important
issues in the image retrieval. The system is said to be
efficient if semantic gap is minimum .The result can
be improved in future by introducing feedback and
user’s choice in the system.
Zhao,Grosky (2002) [18] view that bridging the
semantic gap between the low-level features and the
high-level semantics is within the interface between
the user and the system, other research direction is
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towards improving aspects of CBIR systems by
finding the latent correlation between low-level
visual features and high-level semantics and
integrating them into a unified vector space model.
Peter Stanchev, et.al. [19] proposed that Several
visual descriptors exist for representing the physical
content of images, for instance color histograms,
textures, shapes, regions, etc. Depending on the
specific characteristics of a data set, some features
can be more effective than others when performing
similarity search. For instance, descriptors based on
color representation might be effective with a data set
containing mainly black and white images.
Techniques based on statistical analysis of the data
set and queries are useful.
From [20] a study conclude that a system based on
the fuzzy c-means clustering algorithm, the CBIR
system fuses color and texture features in image
segmentation. A technique to form compound queries
based on the combined features of different images is
devised. This technique allows users to have a better
control on the search criteria, thus a higher retrieval
performance can be achieved.

III.

CONCLUSION

From the literature survey it is concluded
that a wide variety of CBIR algorithms have been
proposed in different papers. The selection feature is
one of the important aspects of Image Retrieval
System to better capture user’s intention. It will
display the images from database which are the more
interest to the user. The purpose of this survey is to
provide an overview of the functionality of content
based image retrieval systems. Most systems use
color and texture features, few systems use shape
feature, and still less use layout features. Fuzzy logic
has been used extensively in various areas to improve
the performance of the system and to achieve better
results in different applications.

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