Content-Based Image Retrieval Features: A Survey

Int. J. Advanced Networking and Applications
Volume: 10 Issue: 01 Pages: 3741-3757 (2018) ISSN: 0975-0290
3741
Content-Based Image Retrieval Features: A
Survey
Anum Masood
Department of Computer Science, COMSATS Institute of Information Technology, WahCantt, Pakistan
[E-mail: anum_msd21@yahoo.com]
Muhammad Alyas Shahid
[E-mail: mashahid79@gmail.com]
Muhammad Sharif
[E-mail: muhammadsharifmalik@yahoo.com]
-------------------------------------------------------------------ABSTRACT-----------------------------------------------------------
Content-Based Image Retrieval (CBIR) systems have been used for the searching of relevant images in various
research areas. In CBIR systems features such as shape, texture and color are used. The extraction of features is
the main step on which the retrieval results depend. Color features in CBIR are used as in the color histogram,
color moments, conventional color correlogram and color histogram. Color space selection is used to represent
the information of color of the pixels of the query image. The shape is the basic characteristic of segmented
regions of an image. Different methods are introduced for better retrieval using different shape representation
techniques; earlier the global shape representations were used but with time moved towards local shape
representations. The local shape is more related to the expressing of result instead of the method. Local shape
features may be derived from the texture properties and the color derivatives. Texture features have been used
for images of documents, segmentation-based recognition,and satellite images. Texture features are used in
different CBIR systems along with color, shape, geometrical structure and sift features.
Keywords: CBIR, Feature Extraction, Feature Selection, Color, Texture, Shape
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Date of Submission: April 06, 2018 Date of Acceptance: April 25, 2018
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I. INTRODUCTION:
With more digital applications, the number of images
produced is increasing rapidly. Huge databases of images
have to be searched to retrieve related images; this is
mentioned as CBIR techniques. A manual search of such
image databases is not only exhaustive but is also not
efficient therefore various approaches have been presented
for the improvement of images retrieval. Accurate
retrieval of images from a lot of digital images is
challenging, therefore, CBIR is considered as an intensive
research area [1-3]. Previously used text-based query
methods are not as efficient and effective as feature-based
methods. The various primitive features such as color,
shape, structure, texture, etc can be used to extract the
similarities between the images already stored in database
and a query by user [4-8].
These features are called low-level features which are
utilized in the search for feature extraction images to
match to the large image database [9-10]. There are
various CBIR systems that use more than one feature.
Color is used in combination with both texture and shape
for improved results [11-12].
1. Applications of CBIR:
CBIR is being used in various fields like biomedicine [13-
14], medicine [15], military [16], and commerce [17], Art
[18,19], Art Galleries [20-21] and Entertainment like
content-basedmultimedia retrieval. For Law enforcement
[22], CBIR is used for face-detection [23-24] copy-
detection [25] while for the control of cyber-crime, CBIR
is used in Image-based CAPTCHA [26-27].In the field of
bioinformatics, it is used for Automatic Annotation [28],
molecular analysis [29], medical diagnosis [30-31] and
face recognition [32-54]. The search engines also use
CBIR for web-search for images [55]. CBIR is widely
being used in biometrics systems. In recent years use of
CBIR systems in various fields has been increased.
In this survey paper, a comprehensive overview of the
CBIR is presented. In Section 2, CBIR Systems are
discussed. Various problem domains of CBIR system are
also mentioned along with the classification of CBIR
systems. Section 3 gives classification is CBIR systems. In
Section 4, few color based techniques used in CBIR
systems are discussed. Techniques using the shape
featurein CBIR systems are mentioned in Section 5. The
summary of various texture based methods over the few
years is shown in Section 6.
II. CBIR SYSTEM:
In any CBIR system, there are mainly three phases i.e.
feature extraction from query image, feature selection and
then the similarity matching. The multi-dimensional
feature vectors are created by the CBIR system and these
feature vectors are matched with those of database images.
The similarity measure or distance measure is done with
the help of some algorithm on which score is calculated.
The threshold value is pre-defined by the system

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developer. Those images which have more score than the
threshold value are extracted and shown as retrieved
results. The Relevance feedback method is adopted so that
whether the user is satisfied or not satisfied by the
retrieved images then the CBIR system is improved to
search keeping in view the selected images by the user.
In Figure 1, a flowchart of CBIR system is shown:
Figure 1: Flow-diagram for CBIR System [69]
2.1. Image Content Descriptor:
An image content Descriptor can be local or global. It can
be specific as well as general. Global uses features of
thewhole image and local divides image into parts first. A
simple method of partition is to use a division i-e cut
image into regions having equal shape and size. They may
not be meaningful and significant regions but it is a
process to represent global features of any image. Partition
the image into similar and homogeneous areas is an
improved method with the use of some standard such as
the Region Segmentation algorithms. Another complex
method is to obtain semantically meaningful objects by
Object Segmentation. The image content is further
classified into two broad categories as visual content and
semantic content.
2.1.1. Visual Content:
The visual content is further classified into two main
classes:
2.1.1.1. General Visual Content:
When the features or content of the query image are
visible and are generally perceived then they fall into this
category. Included common visual contents are the
features like shape, texture, color, structure, spatial
relationship etc.
2.1.1.2. Domain Specific Visual Content:
When the query is based on such content that requires
some domain knowledge then those query image content
are domain specific visual content like Human Face
Detection needs some prior information about the human
facial characteristics. These characteristics are not general;
these are specific i-e only related to human facial features.
2.1.2. Semantic Content:
The semantic contentsare either described by textual
explanation or by using the complex interpretationmeans
which are based on some visual-content.
2.2. Image Retrieval Gaps:
The differences between images stored in database and the
query image in retrieval are called gaps. The degree of
difference will be how far the two images are i-e the gap
between the two images. The gaps are divided into two
categories: semantic and sensory.
2.2.1. Semantic Gaps:
The mismatching of information of visual query data and
the stored image information in the database is obtained.
This selected gape to match the image on the similarities
basis is called semantic gap. User entered some queries for
which optical likeness does not match completely with
human perception. By which a semantic gap between
CBIR system and the user is obtained [56]. Semantic
retrieval has some limitations. A difficulty present in it is
that most of the images have more than one semantic
interpretation. Because images used for training have
usually short description in form of a caption, therefore,
some features might never be recognized. This helps to
decrease the amount of images instances used for training
and weakens the system’s capability to be trained for the
concepts that are rare and which have a high variable
visual appearance. Semantic retrieval system has a limited
vocabulary so it mostly generalizes everything other than
the semantic space i.e. for which is not trained [57].
2.2.2. Sensory Gaps:
These are the gaps between the real object and the
information in form of computational description obtained
from capturing that object in an image form. It is the short-
coming of the image capturing device.
2.3. Problem Domains:
Retrieving images from a varied and huge collection is
difficult. Searching images based on the spatial
relationship of portions of a query image is a not an easy
research problem in CBIR. As segmentation of regions or
objects of an image is not feasible, except in few
applications. Another common problem in CBIR is a
selection of Features.
The divergence between user’s query and the result
displayed by retrieval system images is called Semantic
gap. These semantic gaps are caused due to the low-level
similarity between database images and high-level user-
query image this result in an error from search algorithms.
Image
Collection
Extraction
of Features
Feature
Database
Query
Image
Extraction
of Features
Query Image
Features
Database
Query Image
Features
Retrieved
Image
Feature
Matching

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RF (Relevance Feedback) is the solution for thesame
problem in which the user has the authority to accept or
reject the result and requests for better similarity in output
images.
2.4. CBIR Methods:
There are different methods used in CBIR. Figure 2
represents the most commonly used methods by the CBIR
systems for the image retrieval.
The Features extraction is mostly the initial step in any
CBIR system. Each feature selected is placed in a vector
named Feature Vector. For Feature Extraction some of the
techniques used are the Symbol spotting (graphical shape
extraction), [58] background removal approach [59] key-
points technique [60]. Some other techniques are the usage
of Color, texture, shape, structure, edges as features.
Figure 2: Methods of CBIR Systems [60]
Another method is by the use of Salient region features of
the user-defined image. Few examples are the use of the
global features of the image [61]. Global features such as
the interest points of query image as well as SIFT
descriptor are used in CBIR [62-64]. Image Segmentation
is also done for better region selection. Common methods
are IMALBUM [65], SIMPLIcity, [66] Watershed method
and Region growing method (bottom-up).
Figure 3: Classifications of CBIR Systems [67]
III. CBIR SYSTEM CLASSIFICATION:
As the CBIR systems are increasing in number there was a
need for the classification of these systems. One
classification of CBIR systems is shown in Figure
3.Feature such as texture, color, and shape are most
frequently used in feature extraction phase of CBIR
Systems. The growing need for effective as well as
efficient image retrieval has made the selection of an
appropriate set of features as the most critical step in the
CBIR system formation. In image retrieval research is
usually related to the statistical or general methods for the
properties of the patches of the image.
IV. COLOR FEATURE:
When viewing an image one of the basic features we
observe is color. Color reveals a variety of information.
For usage of color as a feature in CBIR, there has been a
lot of research done such as the color histogram [68-69],
conventional color histogram [70-73], color correlogram,
and color moments as shown in Figure 4.
4.1 Color Space:
A point in any 3D color space is represented in form of a
pixel. Color spaces like RGB, CMY, and HSV (or HSL,
HSB) are widely used. Which color space is the best; there
is no consensus for results of images retrieved.
4.1.1 RGB (Red, Green and Blue) Color Space:
In color combination of RGB each pixel has three values
which are added together to represent a single pixel value
in the color space RGB. Another name for RGB is
Additive Primaries as they are added together for giving
single pixel value. It is device-dependent and non-uniform.
4.1.2 CMY (Cyan, Magenta and Yellow) Color Space:
CMY consist of three color combinations which are Cyan,
Magenta and Yellow. Each pixel has three values which
are subtracted to represent a single pixel value in CMY. In
CMY color space is formed through absorption of light. It
is dependent on the device used and visually non-uniform.
Extraction of Feature Similarity Measurements
Using Salient Regions of
the image
Retrieval based on
Clustering techniques
CBIR Methods
CBIR System
Classification
Query Form (database
image retrieval on basis
of a query)
Image Description
(images stored with
description)
User interactive CBIR
system (User's Authority
for images classification)
Database Content
(Database of CT-Scan
images)

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4.1.3 HSV (hue, saturation (illumination) and value
of brightness) Color Space:
In computer graphics the most used color space is HSV.
The Hue is more suitable for retrieval of an object as it is
not affected (invariant) to the deviations in light intensity
and direction of the camera. A simple formula [74] can
convert the RGB coordinates for an image into HSV
coordinates.
4.1.4 Opponent Color Space:
It the three axes obtain as R-G, 2B-R-G, R+G+B. The
third axis isolates the information about the brightness
which is the advantage of opponent color space. As human
is sensitive to brightness, therefore, we require a color
space which considers the brightness. Opponent Color
Space is not invariant to light intensity and shadows hence
it is better than the HSV color space.
4.2 Uniformity:
When a color space is selected, uniformity a most
important feature is observed [75]. It means that the
viewer observes the two color pairs as the same color with
same similarity distance. Thus the distance measured
among colors should be directly proportional to the
perceptual similarity between them.
4.3 Color Descriptor:
CBIR color descriptor can be local or global. Color space
selection is important for the color descriptor. As a color
space is selected for the representation of the information
of color of a pixel of the query image. Quantization of
color-space means those colors which are very close to
each other and have small distance should be combined
together.
There are certain phrases which are followed when color is
used as the descriptor. First of all, a color space is
designated. Secondly, the quantization of the color space
is completed. After quantization color features are
extracted. These color features collectively are known as a
color descriptor. An appropriate distance function is
derived or any particular CBIR System.
Figure 4: Phases of Color Feature for CBIR [72]
4.4 Color Histogram (CH):
An effective and real representation is a color histogram.
A pixel may be represented as 3 components in the
histogram of color space. For the color histogram, spatial
information about histogram is not considered. This
simple approach is used to split the sub-areas for
histogram calculation [76-78]. The histogram is a
statistical global feature which shows distribution
intensity. Color histograms are widely used in CBIR. [79].
Color feature is often used in a combination of texture and
structure features [80]. Color Histograms are also useful in
recognition of objects. In a large number of images, the
matching of the histogram may saturate discrimination.
Joint Histogram is the solution [81]. Different color
histograms methods have been used for image retrieval.
Few of the color histogram methods are described below:
4.4.1 Conventional Color Histogram (CCH)
It shows the frequency of occurrence of each color (no. of
pixels having that particular color) within a given image.
Every color is connected to its histogram on basis of color
similarity. Dissimilarity basis is not taken into account.
To reduce the number of the histograms, Quantization is
done. Quantization means to merge two colors histograms
which have a similar color. Thus processing time is
lessened. Figure 5 shows the CCH steps.
Figure 5: Different Steps of Conventional Color Histogram [73]
Color Space Selection
Color Space Quantization
Color Feature Extraction
appropriate Distance Function
Derivation
Quantization of
Bins if small color
difference
Frequency
Calculation by
Counting number
of pixel for each Bin
Color Space Division
into Bins

3745
4.4.2 Invariant Color Histograms (ICH)
Invariant Color Histogram is developed on the basis of
color gradients. As the name suggests that it is invariant
under any geometry of surface which is locally affine [82].
4.4.3 Fuzzy Color Histogram (FCH):
During merging of different color histograms with the
slight difference a problem arises. The color may appear to
be similar yet they have a small difference. To overcome
this problem FCH concept was introduced. The color
space is used for relative greenness-redness and blueness-
yellowness) [83].
It also defines the extent of there semblance of the color of
a pixel cube for all histograms fuzzy relationship function
set given by𝜇 . It is stronger to quantization
complications as the changes in in the density of light. The
drawbacks are that it describes only the FCH features and
the global color feature of the image is equally high in
dimensionality as CCH. There is an extra computation for
fuzzy membership function𝜇 .
4.4.4 Color Difference Histogram (CDH):
This is a recent technique for the image color feature
representation [84]. Previously the histogram techniques
only counted the number of frequency i-e the occurrence
of the pixels. CDH takes the total of perceptual difference
in uniform colors between any two points of images under
various backgrounds along with the edge orientation and
color in the color space L*a*b*.
4.4.4.1 Block Encoding of Color Histogram for CBIR
Applications:
Color is a low-level feature yet important feature which is
mostly represented as a histogram in different CBIR
systems. There are different coding approaches for
histogram so that the space used is less and processing is
speedy. Recent research on encoding approaches is done
by [85]. They have used the Golomb-Rice coding which
codes values of the color histogram in floating point by
converting them to integers in some pre-processing steps.
The histogram is mostly shown in the sparse matrix.
4.5 Color Coherence Vector (CCV):
In CCV [86] spatial information is included in a histogram
in a diverse manner. A histogram bin may be classified
into two classes i-e Coherent (used for the uniformly-
colored large region) or incoherent (not used for the
uniformly-colored large region). CCV of an image can be
defined as follows in vector form as in equation – (1):
< , , , , … . . , 𝑁, 𝑁 > (1)
CCV has additional information as compared to Color
Histogram, therefore, it gives better retrieval results.
4.6 Color Correlogram (CC):
It shows the association of colors in terms of distance. CC
is presented as indexed in a table by sets of color where
the probability of occurrence of any pixel of color j at a
distance d from a pixel of I in the given image is
represented by the entry for row (i, j) [87].
The space between pixels P1 and P2 is given by in
equation – (2):
|𝑝 − 𝑝 | (2)
The d with a small value is enough to show special
correlations as local correlations are more important than
global correlation.
CC shows local as well as global spatial information. The
disadvantage of CC is that it has high-dimensionality of
feature space. For reduction of this dimensionality from
(O(N2
d)) to (O(Nd)) a new feature was introduced. It is
known as auto-color correlogram. Auto-correlogram
confines the spatial correlation of identical levels only
which is shown as given in equation – (3)
𝑑
𝐼 = 𝑔,𝑔
𝑑
𝐼 (3)
4.7 Color Moment (CM):
CMare using frequently in CBIR systems which are based
on color features. CM of First Order is normally mean,
Second Order is Variance and the third order is mainly
skewness. These are used in representing color
distributions.
4.7.1 Mean - First Order Color Moment:
It is defined by the equation–(4):
𝜇 =
𝑁
∑ 𝑓𝑁
= (4)
4.7.2 Variance - Second Order Color Moment:
Second order color moment is given by the equation - (5)
given below:
𝜎 =
𝑁
∑ 𝑓 − 𝜇𝑁
= (5)
4.7.3 Skewness - Third Order Color Moment:
Color moment third order (skewness) is given in equation
(6):
=
𝑁
∑ 𝑓 − 𝜇𝑁
= (6)
The value of the ith-color component of any image pixel j
is given by fij and the total no. of pixels of an image are
represented by N. Color Moment is a compact feature as
only three moments for each of the three color component
are required i-e a total of nine values is needed. Although
it is compact yet there is a problem of lower
discrimination power.
4.8 Invariant Color Features:
Color changes with the change in illumination [88].
Variability is considered [89]. Invariance to such
environmental factor is not regarded as part of most color
feature [90-91] Equation for the invariant color feature is
as follows given in equation –(7):
𝑅−𝐺
𝑅+𝐺
,
𝐵−𝑅
𝐵+𝑅
,
𝐺−𝐵
𝐺−𝐵
(7)
It only depends on the surface and the sensor. For shiny
surfaces its equation is given as equation-(8):
|𝑅−𝐺|
|𝑅−𝐺|+|𝐵−𝑅|+|𝐺−𝐵|
(8)
This color-shaped based method considers the color, area
and a new feature called perimeter-intercepted lengths of

3746
the object-segmentation in a given image [92].The image
is segmented into K clusters with the help of K-means
algorithm. The object’s shape is distinguished by the PILs
(perimeter-intercepted lengths). PILs are acquired by the
interception of 8 line-segments from the object perimeter.
All these 8 lines have different orientations yet pass
through the center of the object [93].
Table 1: Comparison of Different Techniques using color as main feature
Sr # Techniques Advantages Limitations
1. CCH [94] Simple
Computation is easy
Sensitive to noise
High-dimensionality problem
Cannot handle rotation and translation
Global spatial information is not encoded
2. ICH[95] Solves the problem of translation and
rotation in image
Invariant under any geometry of the
surface
3. FCH
[96-97]
Encodes degree of similarity of pixel
color by fuzzy-set relationship function
Robust to quantization problems
Robust to light intensity changes
Describes global color properties of the
image only
FCH features are equally high in
dimensionality as CCH
Extra computation for the fuzzy
membership function
4. CCV[98] CCV spatial information
Gives better retrieval results.
Only categorize into two partitions i-e
coherent or incoherent
5. CC[99] Encodes global as well as local spatial
information
Works even for coarse color images
High-dimensionality of feature space
6. CM[100-101] Compact as only nine values is needed
for calculation
Less computation
Compact but lower discrimination power
7. Color-Shape Based
Method (CSBM) [102]
Encodes object shapes and colors More computation
Requires an appropriate color threshold
Sensitive to noise variation
Sensitive to contrast
4.10 Color Image Retrieval Technique based on
Color Feature:
From a database of images, for retrieval of similar images,
an effective and efficient technique was used by [103].
Some color distributions are used with standard and mean
deviation for representation of the global features in any
image. The local characteristics of the image were
represented by a bitmap of the image. This local
characterization increased the accuracy of the image
retrieval system.
V. SHAPE FEATURE:
A basic aspect of segmented regions of an image is its
shape feature. Its representation is important in the
retrieval of similar images [11]. The shape characteristics
are extracted and matched with the database image
collection, therefore; the shape feature representation is
most significant [10].
There are different methods introduced to help in better
retrieval by using different shape representation
techniques [104]. Earlier the global shape representations
were used but with time they move towards local shape
representations as local descriptors produce better retrieval
results [105-106]. Shape representation using discrete
curve evolution for contours simplification removes
irrelevant shape features [107].
5.1. Shape Context (Shape Descriptor):
For similarity matching a shape descriptor called shape
context was proposed which was useful for varying
geometric transformations [108]. Shapes approximation as
concave and convex segments was the approach of
Dynamic Programming for shape matching. The only
problem with it was that there was pre-computation for the
Fourier Descriptor and moments in this technique which
was slow [109].
5.2. Local Shape Features:
In the local shape feature, all the characteristics related to
the geometric details of an image [110]. The local shape is
more related to the expressing of result instead of the
method [111]. Local shape features may be derived from
the texture properties and the color derivatives.
5.3. Scale-Space Theory for Shape Feature:
Scale-Space theory was advised for capturing all the
prominent information, [112]. It gives the basis for the

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detection and recognition of some prominent features and
details on any scale.
5.4. Shape Affine-Invariant Descriptor:
Differential geometric invariant and affine-invariant
descriptor are also available [113]. Color feature and shape
have been combined in invariant method [114-115].
5.5. Object-Shape Feature:
Theoretically, the best approach by segmenting the object
for the object-specific information of imagesis within the
image [116]. It is not necessary to recognize the exact
location of features of the object in an image but it is
important to identify the presence of an object with the
help of its properties. When segmentation is done as
shown in the equation–(9)
𝑥 = °𝑓 𝑥 (9)
The shape of an object is shown in equation-(10):
𝐹 = ∑ ℎ° 𝑥𝑇 𝑗
(10)
Where h shows the functional computing shape and ∑
show the aggregation operation. This equation is for the
accumulative features. Some features have general
applicability like moment invariants and the Fourier
features of objects [117].
For the image retrieval, there is a requirement for the
shape representation that measures the distances of
deformations. Some mismatch images are acceptable in
certain interactive use of retrieval. So compromise on
accuracy may be possible as we need a system to be robust
and computationally efficient [118]. There are
sophisticated methods like elastic deformation of image
portions, multi-resolution representation of shapes and
modal matching techniques [119-120].
5.6. Contour Multi-scale Model:
Multi-scale models of contours have been studies for
database image representation. There are two approaches
to Contour multi-scale model:
a) Multi-Scale Model of Contours as Database
Image Representation
b) Multi-resolution Shape Model for Contours.
These two approaches for contours use are shown in Fig 6.
Figure 6: (a) Steps of a Multi-Scale Model of Contours as Database Image Representation (b) Steps of Multi-resolution
Shape Model for Contours. [121]
5.7. Contour Representation:
The images have to process for retrieval which comprises
a group of operations on the contours. Edge-contours are
represented by the B-spline. There are three detection
types of symmetries: Skew and parallel symmetries are
used for inferring shape from the contour. The third type
of symmetry is smooth local symmetries used for planar
shape description [121].
VI. TEXTURE FEATURE:
In Image processing as well as in computer graphics and
computer vision the texture features are widely used [122]
like wavelet transforms, discrete cosine transform [123]
and curvelet transform. Many textures are composed of the
small textons which are numerous and thus cannot be
perceived as separate objects.
6.1 Structural Pattern Feature:
Various texture features are proposed for analyzing the
structural pattern information in an image. These texture
features include Co-Occurrence of gray-scale values [124],
multi-resolution analysis [125]; Local Pattern Spectrum
[96] and texture unit spectrum-based [126-127]
techniques.
6.2 Gabor-Filter Based Techniques for Texture
Feature:
Popular Techniques include Gabor-filter based techniques
[128-129] Wavelet Decomposition with energy signatures
[130] and Gaussian Markov random field (GMRF)
models. The query image is afterward convolved with
each Gabor functions obtained after the parameter settings.
It has been shown that for the highest texture retrieval
results, Gabor Transform for texture feature is used in
Multi-Scale Model
Contours Extracted from images
Contours smoothed by dividing into
regions (Using second derivative)
Reducing the number of regions
Every contour reduced to an ellipsoid for
characterization
Multi-Resolution Shape Model
Contours sampled by polygon
Simplified by removing points from
contours
Set of Contours points on the boundaries
selected
Point Pairs used for geometrically
invariant contours representation

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CBIR [131]. Results of the Gabor Transform function is
an over‐complete demonstration of the user’s query image
with a certain redundant ratio of K*S (scales*orientation).
6.3 Modified Gabor Function:
Gabor transform has to be applied from all orientations as
it depends on the direction. The angles used have an effect
on the results of the image retrieval. Therefore the existing
Gabor transform had to be modified. The modified Gabor
transform is independent of the angle selection. Its
performance is almost equal to the simple Gabor
Transform.
The modified Gabor Transform is invariant to the rotation.
Because of this invariance for rotation, the modified Gabor
Transform showed better results in almost all the images
[132].
6.3.1 Contourlet Transform for Texture Features:
These are based on directional multi scale filter bank
which deals with the smooth contours of the images.
Contourlets allow various directions at every scale which
in turns gives a critical sampling. They give directionality
as well as wavelets features. Figure 7 expresses the
comparisons of Gabor Wavelet Transform Method and
Contourlet Transform approach is shown.
Figure 7: Comparison of Gabor Wavelet Transform Method and Contourlet Transform Method [127]
6.3.2 Gray-level Run Length Technique for Texture
Feature:
Gray-level run length is useful when analysis at more than
single orientation is to be done for the extraction of
structural information. Spectrum-based techniques are also
not recent [133]. With time signal-processing has attained
progress which results in the efficient power spectrum
tools for analysis. Earlier techniques were gray-level run
length [134] gray-level co-occurrence matrix [122]. Some
models utilize statistical regularities in the texture field
while other models take texture as result of a deterministic
dynamic system which observes noise.
6.4 Techniques for Transformations of Texture
Surfaces:
A collective problem is that for the texture feature
extraction the image with texture surfaces has to undergo
numerous scaling and re-orientation steps but it is a
computationally difficult task. Previously Pyramid
Structured Wavelets [135] tree-structured wavelets [136];
multi-resolution simultaneous autoregressive models were
the multi-resolution technique but the Gabor filters have
shown better performance [137].
6.4.1 Techniques for Illumination Effect on Texture
Surfaces:
The recent algorithms proposed for the texture analysis
consider the non-ideal conditions like illumination
intensity change due to the source itself or the capturing
device, texture viewing angle change and spectral
attributes of light [138]. In the real-world scenario, there
are certain environmental factors like changes of light
directed from source [139]. Changes in the spectral
attributes of light also have an effect on the texture of the
image surface affecting the inter-class distances as well as
the intra-class of the textures [140].
Gabor Wavelet Transform
S Scales and K orientations
Rotation and dilations of 2D
Gabor function
obtained K*S Gabor Functions
Image convolvolation with
Gabor Function
Representation of original
image with K*S redundant
ratio
Contourlet Transform
Bandpass images from
Laplacian Pyramid
Fed to Directional Filter Bank
Directional information
obtained
Low frequency components
separated
Redundancy ratio is less as
directional sub-bands are
decimated

3749
6.4.2 Algorithms for Changes in Affine
Transformation and Light Attribute:
Affine Transformations like rotation and scaling of images
affect the texture surfaces [141-142], the non-affine
transformations like 3D orientation changes also affect
texture characteristics [143]. In the real-world, the 3D
nature of the textured surface may significantly change the
observed texture data by light direction change [144].
Some of the recent texture analysis algorithms are LPB
uniform for the changes in spectral attributes of light and
LPBrotational invariance for the changes in affine
transformations [145].
The common texture analysis algorithms are Gray-Level
Co-occurrence matrix (GLCM), Gabor filters, LBP with
Rotation Invariance, Local Binary Patterns (LBP), LBP
with Contrast Measures, LM 3-D Textons [146], MR8-
Textons, Multi-scale Opponent Color Representation and
Multi-resolution Histogram [147]. In graphics field, there
are researches in the texture synthesis field where on the
adjacent orientation of pairs of wavelet coefficients in the
images, the statistical descriptor is based. To extract the
texture features the coefficients of a transform are used on
the original pixel values of an image by statistical
computations [148].
6.5 Texture Descriptor:
Texture feature was used for image retrieval even in
1996[149]. Texture Descriptors encoded general visual
attributes into a numerical form which can be easily used
for the computational tasks [150]. Some advance textured
descriptors are affine transform and photometric
transformation invariant features [151]. Improved affine-
invariant transform designed for the texture recognition
were used in point detection [152].
6.5.1 Wavelet Transform:
Wavelet feature performs well when the texture images
were rotated [153]. Wavelets have been used for their
compression efficiency and their locality. The various
wavelet transforms generates series of rotation and
dilations. Lowest levels of wavelet transforms are used for
the texture representation [154-156]. A comparative study
of the early works done on texture classification from
transform-based characteristics is done [157].
6.5.2 Wavelet Packet Signature Methodology:
A generalization of supported wavelets is Wavelet packets
[158]. The use of wavelet packet was initiated by Coifman
et al.[159].The wavelet packets are ortho normal [160] and
are localized in both frequency and time and thus can be
used an alternate for the Fourier (frequency based)
analysis. The methodology for the texture classification by
the help of wavelet packet representation was introduced
[154]. The minimum-distance classifier used was
Euclidean Distance. Texture Classification by Wavelet
Packets Signature Methodology is shown in Figure 8.
Figure 8: Texture Classification by Wavelet Packets Signature Methodology [151]
6.5.3 Multi-Resolution Wavelet Packet Method:
Texture signatures that were based on the multi-resolution
wavelet packet method showed potential for accurate
classification and discrimination. For texture
segmentation, the methods of scale-space analysis which
was done using this method gave efficient representation
[161].
6.5.4 Wavelet Packets and Fuzzy Spatial Relations:
In CBIR system the relevant database images with respect
to the user’s defined image are recovered using the
features like texture, color, and shape [162]. But there was
a need for more sophisticated image retrieval system for
this a method based on the wavelet packets was
introduced. The Fuzzy similarity measure was introduced
in FIRM [163] in which fuzzy features were used. Wavelet
transform provides structure for analysis and at different
scales properties of images [164].
6.5.5 Local Spatial Frequency Properties for Texture
Features:
Local spatial frequency properties can be utilized to obtain
attributes of texture. These attributes can be shared in the
wavelet area by wavelet filter for convolution in the spatial
area. For the extraction of colored texture mixing of
wavelet features was done [165]. For multi-resolution
Feature Extraction, the arbitrary tree structure was used,
analysis of multi-resolution considers the spatial
similarities across sub-bands [166].
6.5.6 Application of Texture Features:
Texture features have been used for images of documents,
[167] segmentation-based recognition, [168] and satellite
images. [169] Texture features are used in various CBIR
systems along with color, shape, geometrical structure and
sift features.
6.6 Color Co-Occurrence Matrix for Texture
Feature Extraction:
A unique method is introduced which is used to color co-
occurrence for extraction of texture feature and for
Texture Selection and
Sampling
Partition of Wavelet Packet
Space
Discrimination Using Simple
Minimum-Distance
Classifier
Discrimination Using a
Neural Network Classifier

3750
measuring similarity into two color images [170]. The
feature obtained gives information about the texture
correlation [171] and color information. This technique
gave better result in terms of retrieval accuracy (recall and
precision) as compare to the GLCM and histogram
technique.
Classification of various methods used for feature
extraction mostly comprises of two main categories
statistical based feature extraction and structural based
feature extraction. Statistical feature extraction methods
are a morphological operator, adjacency graph, and co-
occurrence matrices while statistical feature extraction
methods are world decomposition and Markov random
field. The categories are shown by the help of following
diagram [172-173]. Categories of Feature Extraction
Methods are given in Figure 9.
Figure 9: Categories of Feature Extraction Methods [172-173]
VII. CONCLUSION:
Large databases of images have to be searched to retrieve
query related images with the help of CBIR System. A
manual search of such image databases is inefficient and
exhaustive. Accurate retrieval of images from numerous
digital images is challenging, therefore, CBIR Systems is
an intensive research area. Semantic Gap is a deviation
from the information that is acquired from visual query
data and the already stored information in the database on
the similarities basis. For better retrieval of images from
databases, certain features are used. Feature such as shape,
color and texture are most generally used in feature
extraction phase. Growing need for effective image
retrieval has made the feature selection phase the most
critical step. This survey paper consists of the brief
introduction of features of CBIR system. Color feature is
observed at first. Different color spaces are defined in
research work related to the use of the color feature in
CBIR system. A comparison of different techniques using
color as the main feature is presented in this survey. The
shape is the basic characteristic of segmented regions of
an image. The features of shape are extracted and matched
with features already saved in database for images. Global
shape representations, as well as local shape
representations, are used forshape descriptors produce
better retrieval results. Local shape descriptors are related
to the geometric details of an image. Local shape features
may be derived from the texture properties and the color
derivatives. Texture features are used in various CBIR
systems along with color, shape, geometrical structure and
sift features. Some advance textured descriptors are affine
transform and photometric transformation invariant
features. Improved affine-invariant transform designed for
the texture recognition were used in point detection. A
common problem with texture feature extraction is that
the image with texture surfaces has to undergo numerous
scaling and re-orientation steps which are computationally
difficult tasks. Previously Pyramid Structured Wavelets,
tree-structured wavelets, multi-resolution simultaneous
autoregressive models were the multi-resolution technique
but the Gabor filters have shown better performance.
Some recent work in the field of CBIR features is also
discussed in the survey paper.
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AUTHORS’ PROFILES
Anum Masood, Lecturer,
Department of Computer
Science, CIIT, Wah
Campus, received her
Master of Computer
Science and MS (CS)
degree from COMSATS
Institute of Information Technology, WahCantt. She is
now serving as Lecturer, Department of Computer
Science, CIIT, Wah Campus. Her areas of interest are
Image Processing, Computer vision, and Medical Imaging.
Muhammad Alyas Shahid received
his Master of Computer Science
degree in 2002. He received his
MS(CS) in 2017 from COMSATS
Institute of Information Technology,
WahCantt with specialization in
Image Processing. He is into
teaching field from 1998 till date.
Currently he is serving as a Lecturer of Computer Science in
POF Institute of Technology, WahCantt. His research interests
are Image Processing, Multimedia Processing, and Computer
Networks & Security.
Muhammad Sharif, PhD,
Associate Professor COMSATS
Institute of Information
Technology, WahCantt received
his MSc in Computer Science from
Quaid-e-Azam University,
Islamabad. He received his
MS(CS) and PhD(CS) from
COMSATS Islamabad with specialization in Image Processing.
He is into teaching field from 1995 till date. His research
interests are Image Processing, Computer Networks & Security,
Parallel and Distributed Computing (Cluster Computing) and
Algorithms Design and Analysis.

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