ISE: Interactive Image Search using Visual Content
Mohamed Hamroun
, Sonia Lajmi
, Henri Nicolas
and Ikram Amous
Department of Computer Science, Bordeaux University, LABRI Laboratory, Bordeaux, France
Department of Computer Science, Sfax University, MIRACL Laboratory, Sfax, Tunisia
Department of Computer Science, Al Baha University, Saudi Arabia
Keywords: CBIR, Genetic Algorithm GA, Retrieval Image.
Abstract: CBIR (Content-Based Image Retrieval) is an image retrieval method that exploits the feature vector of the
image as the retrieval index, which is based upon the content, including colors, textures, shapes and
distributions of objects in the image, etc. The implementation of the image feature vector and the searching
process take a great influence upon the efficiency and result of the CBIR. In this paper, we are introducing a
new CBIR system called ISE based on the optimum combination of color and texture descriptors, in order to
improve the quality of image recovery using the Particle Swarm Optimization algorithm (PSO). Our system
operates also the Interactive Genetic Approach (GA) for a better research output. The performance analysis
shows that the suggested 'DC' method upgrades the average precision metric from 66.6% to 89.50% for the
Food category color histogram, from 77.7% to 100% concerning CCV for the Flower category, and from
44.4% to 67.65% regarding co-occurrence matrix for the Building category using the Corel data set. Besides,
our ISE system showcases an average precision of% 95.43 which is significantly higher than other CBIR
systems presented in related works.
Nowadays, image-based practical apps have
become available everywhere, whether on TV
channels, in newspapers, museums and even
among. Internet search engines that suggest image
search solutions. These images indexing and
retrieving depend mainly on text annotations or text
elements that can be attributed to them. In many
cases (TV channels, newspapers, etc.), the archiving
of images and video recording is done only through
a manual annotation step using keywords. This
indexation represents a long-term and recurring task
for humans, especially with the image bases
increasingly growing. Moreover, this task depends
highly on each person's culture, knowledge and
In the other hand, with the massive escalation in
the number of videos accessible to the public thanks
to technical progress, the prices of memory supports
have witnessed a dramatic decline over the last
decade while their storage capacity has sensibly
risen. This availability also gave rise to the creation
of several storage possibilities in computing
systems to keep up with the development of video
files. However, a subsequent growth in exploitation
tools is also needed to allow the user to access and
handle these documents efficiently. It is within this
framework that CBIR systems have proven to be of
a high efficiency for researchers as they have been
conceived to ensure “an automatic indexing and
searching system” which is able to “retrieve an
image based on its visual features” (Kundu et al.,
2015), (Yue et al.,2011).
Considering this context, a visual content image
search system needs to be established. In the
literature CBIR several systems are proposed
extract the image features with innovative methods
(Singha et al., 2012), (Sandid et al., 2015), (Farsi et
al 2013). The main limitation of the proposed works
is the fact that they don’t consider the user feedback
to improve the result of the image retrieval. In fact,
this consideration can be made using genetic
algorithm. In this paper, a new CBIR method based
on genetic algorithm is proposed.
The innovative aspects of the proposed method
are as follows:
Combine usual descriptors features to obtain a new
DC descriptor.
Hamroun, M., Lajmi, S., Nicolas, H. and Amous, I.
ISE: Interactive Image Search using Visual Content.
DOI: 10.5220/0006806702530261
In Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018), pages 253-261
ISBN: 978-989-758-298-1
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Optimize the result of DC descriptor by applying the
genetic algorithm initially proposed by Holland
(Holland., 1975).
The rest of this paper is organized as follows:
Section 2 focuses on some important related works.
The proposed CBIR system is described in section
3. Experimental setup and results are presented in
Section 4, finally, in Section 5, we conclude with
the summary of this paper
Among the most important recognition aspects are
color features. Color in these applications is a solid
value because it remains an unchanging parameter
that does not alter when the image orientation, size
or placement is altered (Dubey et al.,2015). CBIR
systems use conventional color features such as
dominant color descriptor DCD (Wang et al.,2011),
color coherence vector CCV (Pass et al.,1996),
color histogram (Singha et al., 2012) and color
auto-correlogram (Chun et al., 2008). DCD is about
quantifying the space occupied by the color feature
of an image by placing its pixels into a measurable
number of partitions and calculating the means and
ratio of this placement. CCV, however, partitions
the image histogram bins into coherent or
incoherent types. The results of this method are
more precise in that they not only emanate from
color histogram classification but also from spatial
classification. The accuracy of these results is more
palpable when it comes to images that contain
rather homogeneous colors (Pass et al.,1996).
Another important recognition aspect is the
image texture. Among the most elemental features
of an image, we may note the way in which its
different regions are arranged. The analysis of
texture can provide substantial information about
the relationship between the neighbor regions
(Sandid et al., 2015), (Rashno et al., 2015). This
analysis concerns such common features as those
which can be classified into four categories:
statistical, structural, model-based and signal
processing-based features. These latter have been
the most widely used because of their efficiency
(Farsi et al 2013). Indeed, among the most used
methods of signal processing-based features are
Discrete Cosine Transform (DCT), Discrete Sine
Transform (DST), Fourier transform Gabor Filter,
Wavelet Transform and Curvelet Transform.
The process with which CBIR systems function
starts with feature extraction which is an important
phase. This extraction is launched with low level
features such as color features, which are considered
among the primary and most eminent ones. DCD is
a pertinent and intuitive color representation as it
adopts an effective and concise method to describe
the color distribution within an image (Wang et
al.,2011). Both color and textural hybrid features
were suggested in (Shiv et al.,2015a); these are
referred to as rotation and scale-invariant hybrid
descriptors (RSHD). The first step in this method is
to distribute the RGB color space into 64 partitions
so as to quantize the image. Afterwards, the adjacent
structural patterns are employed to vehicle the
texture information of the image. Another
dimension adds up to DCD and the spatial color
descriptors which is the semantic feature (Talib et
al.,2013). This latter is employed to bridge the gap
between the two previously mentioned descriptors.
Then, according to the color of each image
component and background, the most dominant
colors are appointed with different weights.
The system extracts BDIP and BVLC features in
(Young et al., 2003) as textural features. In (Yildizer
et al.,2012), the system starts with resizing the
images into a 128x128 format then applies the
wavelet transform to them in 4 levels. The items
employed as feature vector are the standard
deviation of components in levels 3 and 4 and the
LL component. A local wavelet pattern, which is a
texture feature descriptor, was proposed in (Shiv et
al., 2015b). In order to construct the descriptor, the
local wavelet pattern relies on the connection
between the local neighbors and the center pixel on
the one hand and the circumambient neighbors on
the other. In (Shiv et al.,2015c), new local patterns
were brought in: the BoF-LBP. This method
operates so as, first, to filter the images using the
bag of filters (BoF), then to compute the local binary
pattern (LBP) over each filtered image and, finally,
to concatenate them in order to determine the BoF-
LBP descriptor. In (Murala et al., 2012), local tetra
patterns (LTrP) were introduced in a way in which
the connection between the referenced pixel and its
adjacent ones is used so as to allow the computing
of texture descriptors.
Fig.1 shows the overall architecture of the proposed
image search system. After preparing the dataset
(Corel), The user can introduce a query image. A
step of features extraction is applied on the image
ICEIS 2018 - 20th International Conference on Enterprise Information Systems
query (on-line). The used descriptors are explained
in detail in section 3.1. The extracted descriptors
are after combined to a new descriptor called DC
(Descriptor Combination). In the section 3.2 we
explain how we combine the extracted features to
obtain this new descriptor DC. This process of
indexation is applied on the the same images in our
database in an offline mode. A step of matching
between query vectors and collection vectors is
made to obtain the DC vectors. The result is
classified according to their degree of relevance
with the query vectors.
To improve the result of indexation and search
(section 4), we apply the genetic algorithm to the
new DC descriptor. Genetic algorithms are the
result of the works of Holland (Holland., 1975),
(Goldb et al., 1989) in the seventies of the previous
century. GAs takes their inspiration from the
Darwinian vision of the biological evolution.
Indeed, the biological evolution favors individual
organisms which are tolerant to variations. The
individuals which are the most resistant to the
variations of the environmental have more chance
to persist and to impose their offspring along the
generations. The adaptation of every individual is
measured according to a fitness measure
representing an objective value taking into account
all the constraints of the problem. As defined by
Holland, the GA consists of three steps: selection,
crossover and mutation.
Later, we will focus on upgrading the results of
this method by implementing the genetic algorithm.
The application of the genetic algorithm is as
1. Initial population generation phase: Vectors
resulting from the DC descriptors.
2. Evaluation and Selection phase: vectors having
a distance greater than or equal to 0.6 with respect
to the query image descriptor vector using Equation
1 of 3.3.
3. Crossover phase: We can realize this process by
cutting twoo strings at a randomlyy chosen position
and swapping thee two tails. This process, whichh
wee will call one-point ccrossover in the following,
is visualized in Fig.2. We will apply the same
principle on descriptor vectors.
4. Mutation phase: Is the occasionall introduction
of new features in to tthe solution strings of the
population pool to maintain diversity in the
Though crossoverr has the main responsibility
to search for the optimal solutionn, mutation is also
Figure 1: Conceptual Architecture of Search System “ISE”.
ISE: Interactive Image Search using Visual Content
Figure 2: One-point crossover of binary strings.
used for thiss purpose. We will applyy the same
principle on descriptor vectors
Figure 3: Mutation.
5. Matching: comparing between the cross and
mutation resulting vectors and those assigned to
describe each image of our base and returning the
mot similar vectors. Then, we select the first 20
6. Displaying results in the form of images
conforming to the 20 selected vectors.
7. Stop Criterion: All result images must have a
distance greater than or equal to 0.85 in regards to
the query image (otherwise returning to step 2).
3.1 Used Descriptors
3.1.1 Color histograms
Color histograms are frequently used to compare
images. Examples of their usage in multi-media
applications includes scene break detection (Arun et
al., 1995), (Kiyotaka et al., 1994) and image
database query (Brown et al., 1995), (Myron et al.,
1995), (Virginia et al., 1995), (Alex et al.,1996).
Their popularity stems from several factors. These
factors are listed in the following:
Color histograms are computationally trivial to
Small changes in camera viewpoint tend not to effect
color histograms.
Different objects often have distinctive color
Researchers in computer vision have also
investigated color histograms. For example, Swain
and Ballard (Michael et al.,1991) describe the use of
color histograms for identifying objects. Hafner et
al. (James et al., 1995) provide an efficient method
for weighted-distance indexing of color histograms.
Stricker and Swain (Markus et al., 1994) analyze the
information capacity of color histograms, as well as
their sensitivity.
3.1.2 Color Coherence Vectors
The color coherence vector CCV represents another
more detailed variant of the color histogram. The
concept of coherence is linked to a pixel belonging
to a considerable size space. Conversely, an
incoherent pixel is isolated or belongs to an
insignificant size space. The color coherence vector
represents this classification of image colors (Greg
et al., 1996). The concept of space used
hereinbefore refers to a zone of identical color. A
labelling technique of connected components
enables the generation of regions and adjacency
interconnections used of corresponding type 8
(which includes diagonal adjacencies). Pass puts
forward precising the threshold beyond which a
space is considered coherent at 1% of the image
total size (Pass et al., 1996). Ai refers to the number
of coherent pixels in the row of color, while βi refers
to the number of incoherent pixels. An image CCV
is defined by a vector [(α1, β1) (α2, β2) ... (αn, βn)].
The addition of vectors (α1 + β1, α2 + β2... αn + βn)
results in the image color histogram.
The key strength of this approach lies in adding
spatial information to the histogram through their
refinement. This onset delivers more reliable results
than those directly derived from histograms
analysis. Even with a conventional distance
between vectors, this approach consistently delivers
good results. Still, it has the drawback of amplifying
sensitivity towards light conditions.
3.1.3 Co-occurrence Matrix
The greyscale co-occurrence matrices of an image
pixels is the most popular statistical technique
(Chen et al.,1979), (Marceau et al.,1990) to extract
texture descriptors for various types of images. For
instance, the segmentation and classification of
images of different types, such as medical images,
aerial and astronomical etc. This approach involves
exploring the special texture dependencies by
constructing a co-occurrence matrix first, based on
the orientation and distance between the image
pixels. The success of this process depends on
parameter proper choice including: the size of the
matrix on which the measurement is made, and the
distance between the two pixels of the pattern.
ICEIS 2018 - 20th International Conference on Enterprise Information Systems
3.2 Creation of a New "DC"
It goes without saying that a set of features applied
to different image types does not necessarily lead to
the same results. In other words, a set features which
issues a precise retrieval result for a given image
type may lead to insufficient results when applied to
another category and vice versa.
Table 1: Average precision based on Color histogram,
CCV and Co-occurrence matrix for Corel database.
Corel-1K C_HIST CCV Co_Matrix
Africa 88.8 33.3 44.4
Beach 66.6 66.6 22.2
66.6 44.4 44.4
Bus 55.5 33.3 22.2
100 66.8 88.8
33.3 11.1 22.2
33.3 77.7 33.3
Horse 77.7 55.5 22,2
Mountain 11.1 55.5 55.5
66.6 44.4 66.6
e 59.9 51 42,1
The previous table may lead us to the conclusion
that color histogram provides more efficient results
when applied to the themes of “Africa” and
“Dinosaur”. On the other hand, better results are
obtained from applying CCV on the themes of
“Flower” and “Mountain”. The inefficiency of each
set of features when applied to certain categories
can be restituted by their efficiency or the
effectiveness of their combination with other sets of
features. The PSO algorithm may be employed in
this sense to compensate for such deficiencies.
Introduced by Kennedy and Eberhart, the PSO
algorithm has proven to be an adequate solution for
different optimization problems (Eberhart et
al.,1995). Indeed, PSO operates by modeling the
swarm intelligence behavior and finding in the
search area the most suitable solution. Each particle
in the search area is treated as a potential solution.
The observed particle relies on a fitness function to
imitate the adjacent particles and stores the optimal
solution at the local level (local maxima) and the
optimal solution at the global level (global maxima).
Furthermore, each particle acts so as to drift to more
efficient solutions that best fit its own velocity. This
latter is given by the calculation of movements
towards local and global maxima.
Color histogram, CVV and coocurrence matrix are
the three sets of features that are used in our case.
For each of these sets, a corresponding similarity
measure is computed. The following diagram shows
how the final similarity measure is computed thanks
to the incorporation of the three similarity measures
explained in Figure 4:
Figure 4: Simplified schema of the DC algorithm.
The three corresponding similarity measures
associated with the feature sets Histogram color,
CVV and co-ocurrence matrix are respectively
assigned the weights α, β and γ. Moreover, the PSO
algorithm is applied to 50% of the database (as the
training data) to compute the weights. Indeed, the
average precision of CBIR corresponds to the
fitness function of the PSO algorithm and the
particles of this latter are 3D dimension variable (α,
β and γ). Hence, while the PSO algorithm aims at
finding the variables α, β and γ, it positively affects
the average precision of the CBIR system by
maximizing it. The Algorithm below represents the
PSO (Eberhart et al.,1995) algorithm for feature
Algorithm : PSO.
Let S bee the number of particles, x i be the best
Known position of particle i and x be the best
known position of the eentire swarm. The
proposed feature algorithm based on PSO is as
1. Parameter Initialization:
Forr each particle i = 1,2, ..., S do:
2. Initialize the weight (w), the numberr of
iterations, the maximum velocity (V max), the
acceleration coefficients c 1 and c 2 and the
ranks of the particles For Each dimension.
3. Initialize the particle'ss position (x i ) with a
ISE: Interactive Image Search using Visual Content
4. Initialize the particle's best position Known to
its original position: xi xi.
5. Calculate the average CBIR precision for all
particles and find the swarm's best known
position (x).
6. Initializee the particle'sz velocity: v i ~ U
(v max, v max).
7. Until the numberr of iterations performed or
the average CBIR precision value is found,
repeat: For each particle i = 1,2, ..., S do:
8. Pick random vectors r 1, r 2 ~ U (0,1).
9. Calculate v i (t + 1) = wv i (t) + c 1 r 1 (x i -
x i (t)) + c 2 r 2 (x - x i (t))
10. Calculate x i (t + 1) = x i (t) + v i (t + 1)
11. Calculate the average CBIR precision for
x i (t + 1) and it Refer to P (x i (t)).
12. If P (x i (t + 1))> p (x i) then update the
particle's best position Known: x i x i (t + 1).
13. If P (x i (t + 1))> p (x i) then update the
swarm's best Known position: x x i (t + 1).
In the above Algorithm, v I stands for the velocity,
w controls the interaction of power between the
different particles, while c 1 and c 2 lead the
particles into the right directions. In addition,
r 1 and r 2 are chosen as random variables that
illustrate the idea of stochasticity in the PSO
method, x i stands for the position of local maxima
and x stands for the position of the overall maxima.
3.3 Similarity Measure
The process returns to measuring the similarity
between two images to judge the similarity or
dissimilarity. Thus, after representing by vectors the
extracted characteristic of the query image and the
others images from the database,, We adopt the
Euclidean distance to measure the distance between
these vectors, in our application, we use the
Euclidean distance thanks to its simplicity of
calculation of the similarity. This distance is
calculated according to the following formula:
(, ) ( )
Dxy x y
X and y: two images (one query image and the other
is an image of the database).
Xi: The query image feature vector,
Yi: The current image feature vector.
Xi-yi: refers to a vector that corresponds to the
discrepancy between the vectors xi and yi.
The most important evaluating metrics for CBIR
performance analysis are precision and recall
indexes which are defined as follows:
Number of relevant images retrieved
Precision (2)
Total number of images retrieved
Number of relevant images retrieved
Recall (3)
Total number of relevant images in the collec
In experiment, all images of each category are
presented as a query image separately, and then the
precision of the first 20 retrieved images are
computed for each query. Finally, the average
precision of all queries are computed and reported
for each category.
Table 2: Comparison of the average precision of the previous methods and proposed method.
DB Semantic
Average (%)
REF Sadegh
et al.,
et al.,
et al.,
et al.,
Shiv et
Shiv et
Africa 72.40 68.30 54.95 49.95 73.05 68.95 59.90
Beach 51.15 54.00 39.40 71.25 59.35 41.10 50.85
79.5 89.50
Building 59.55 56.15 39.60 30.10 61.10 74.30 50.15
67.65 87.10
Bus 92.35 88.80 84.30 79.75 69.15 64.40 94.00
Dinosaur 99.90 99.25 94.70 92.05 99.15 99.55 97.60
Elephant 72.70 65.80 36.00 59.45 80.10 56.65 46.65
Flower 92.25 89.10 85.85 99.50 80.15 86.55 87.50
Horse 96.60 80.25 57.50 82.25 89.10 93.20 76.50
Mountan 55.75 52.15 29.45 54.60 58.00 55.15 35.25
Food 72.35 73.25 56.70 20.20 74.50 77.95 56.25
Average 76.50 72.70 57.85 63.91 74.36 71.78 65.47
ICEIS 2018 - 20th International Conference on Enterprise Information Systems
Figure 5: Comparison of the average precision of the previous methods and proposed method.
Figure 6: Image retrieval results for Flower a. (Kundu et al., 2015), b. our proposed system ISE.
The overall performance of ISE system with DC
feature and Genetic algorithm is compared with
some state-of-the-art CBIR systems.
The average precision for all image categories of the
Corel-1k dataset is reported in Tab. 2. To show the
utility of our CBIR scheme, the results of nine other
(Sadegh et al., 2017) (Shiv et al.,2015) (Chuen et al., 2009)
(Shiv et al.,2015a) (Murala et al., 2012) (Yildizer et al.,2012)
(Kundu et al., 2015) DC ISE
ISE: Interactive Image Search using Visual Content
CBIR systems are also reported in this table. Since
the average precision of our results is %95.43, our
CBIR scheme has the highest accuracy among the
other state-of-the-art CBIR systems.In fact, our
proposed CBIR system outperforms, (Chuen et al.,
2009), (Talib et al.,2013), (Yildizer et al.,2012),
(Kundu et al., 2015), (Shiv et al.,2015a) and (Shiv
et al.,2015c).
The results are depicted in Fig.6. These primary
results show that our ISE scheme has better
performance results by retrieving20 images
correctly among the flower category. On the other
hand, the results are17 images for the CBIR of ref
(Kundu et al., 2015)
According to the results assessment of an in-
depth testing that we have performed, we could
actually say that our visual content search system
succeeded in demonstrating its reliability and
accuracy. These tests enabled us to recognize
performance of the new DC descriptor, defined in
this article, and of the genetic algorithm for image
search. It can be concluded that our ISE system
succeeded, to a certain extent, in achieving our
target to improve search by visual content.
In this paper, we have validated our image search
system proposal based on the Corel test database.
We have developed an image search system called
ISE allow users to easily access the desired
images starting from image query. The innovative
features of our new ISE image search system are (i)
Defining a new descriptor "DC" and (ii) Applying
the genetic algorithm in image search. The
application of the genetic algorithm is made to
improve results returned by the DC descriptor.
Despite the results that we achieved, the existing
visual content image retrieval systems are focusing
on addressing particular issues including semantic
insufficiency during indexation and retrieval.
However, only a few works are interested in
merging visual and semantic contents. Accordingly,
developing approaches that focus on this boundary
has become necessary. We will therefore tackle this
problematic by suggesting a method of image and
video documents searching based on a multi-level
fusion of visual and semantic.
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