Combining Generative And Discriminative Classifiers For Semantic Automatic Image Annotation

Brahim MINAOUI, Mustapha OUJAOURA & Mohammed FAKIR
International Journal of Image Processing (IJIP), Volume (8) : Issue (5) : 2014 225
Combining Generative And Discriminative Classifiers For
Semantic Automatic Image Annotation
Brahim MINAOUI bra_min@yahoo.fr
Faculty of Science and Technology,
Computer Science Department,
Sultan Moulay Slimane University.
PO Box. 523, Béni Mellal, Morocco.
Mustapha OUJAOURA M.Mustapha.Oujaoura@ieee.org
Mohammed FAKIR fakfad@yahoo.fr
Abstract
The object image annotation problem is basically a classification problem and there are many
different modeling approaches for the solution. These approaches can be classified into two main
categories such as generative and discriminative. An ideal classifier should combine these two
complementary approaches. In this paper, we present a method achieving this combination by
using the discriminative power of the neural networks and the generative nature of Bayesian
networks. The evaluation of the proposed method on three typical image’s database has shown
some success in automatic image annotation.
Keywords: Automatic Image Annotation, Discriminative Classifier, Generative Classifier, Neural
Networks, Bayesian Networks.
1. INTRODUCTION
Automatic image annotation help to bridge the semantic gap, that exists between low-level visual
features and the high-level abstractions perceived by humans, by producing object labels or
keyword annotations which are nearer to the high level semantic descriptions needed for good
image retrieval.
In order to overcome this semantic gap, a number of current research efforts focus on robust
classifiers achieving automatically multi-level image annotation [1-6]. These classifiers can be
characterized as generative and discriminative according to whether or not the distribution of the
image and labels is modeled.
It was observed that generatively-trained classifiers perform better with very few training
examples and provide a principled way of treating missing information, whereas a classifiers
trained discriminatively perform better with sufficient training data and provide a flexible decision
boundaries [7]. Motivated by these observations, several researchers have proposed a variety of
techniques that combine the strengths of these two types of classifiers. These hybrid methods,
which have delivered promising results in the domains of object recognition [8-10], scene
classification [11-15] and automatic image annotation [16-17 ], have been explored in different

ways: [9] and [11] propose a classifier switching algorithm to select the best classifier (generative
or discriminative) for a given dataset and availability of label. [10], [14] and [15] propose a
technique for combining the two classifiers based on a continuous class of cost functions that
interpolate smoothly between the generative strategy and the discriminative one. [8, 12-13] and
[16] propose a hybrid generative-discriminative approach in which the features extracted from a
generative model are analyzed by a followed discriminative classifier. [17] devise a hybrid
generative-discriminative learning approach that includes a Bayesian Hierarchical model
(generative model) trained discriminatively.
In this paper, in an attempt to gain the benefit of both generative and discriminative approaches,
we propose an approach which combines in a parallel scheme the Bayesian networks for the
generative model and the neural networks for the discriminative classifier to accomplish the task
of automatic image annotation. The annotation decision is realized by the vote of combined
classifiers. Each classifier votes for a given keyword. The keyword that has the maximum of votes
will be considered as the proper keyword for the annotation of an object in a query image.
The rest of paper is organized as follows. The various features used in this study are explained in
Section 2. Section 3 presents the Bayesian networks and neural networks classifiers. Section 4
describes the experiences adopted to realize the automatic image annotation using these
classifiers. Finally, the conclusion of this work is presented in Section 5.
2. FEATURES EXTRACTION
After dividing the original image into several distinct regions that correspond to objects in a scene
by using region growing segmentation algorithm [18], the following descriptors are extracted:
2.1 Color Histogram
Typically, the color of an image is represented through some color model. There exist various
color models to describe color information. The more commonly used color models are RGB (red,
green, blue), HSV (hue, saturation, value) and Y, Cb, Cr (luminance and chrominance). Thus, the
color content is characterized by 3 channels from some color models. In this paper, we used RGB
color models. One representation of color image content is by using color histogram. Statistically,
it denotes the joint probability of the intensities of the three color channels [19].
Color histogram describes the distribution of colors within a whole or within an interest region of
image. The histogram is invariant to rotation, translation and scaling of an object but the
histogram does not contain semantic information, and two images with similar color histograms
can possess different contents.
The histograms are normally divided into bins to coarsely represent the content and reduce
dimensionality of subsequent classification and matching phase. A color histogram H for a given
image is defined as a vector by:
{ }[ ]
( ) ( )( )
( ) ( )




















×<≤





×−
×
−
=∈=
∑ ∑
−
=
−
=
k
EiiC
k
Eiand
NM
iCyxf
kihH
M
x
N
y 256256
1
,
,...,1
1
0
1
0
δ
(1)
Where:
• i represent a color in the color histogram;
• E(x) denotes the integer part of x;
• h[i] is the number of pixel with color i in that image;
• k is the number of bins in the adopted color model;

And δ is the unit pulse defined by:
( )



≠
=
=
00
01
xsi
xsi
xδ (2)
In order to be invariant to scaling change of objects in images of different sizes, color histograms
H should be divided by the total number of pixels M x N of an image to have the normalized color
histograms.
For a three-channel image, a feature vector is then formed by concatenating the three channel
histograms into one vector.
2.2 Legendre Moments
In this paper, the Legendre moments are calculated for each one of the 3 channel in a color
image. A feature vector is then formed by concatenating the three channel moments into one
vector.
The Legendre moments [20] for a discrete image of M x N pixels with intensity function f(x, y) is
the following:
∑ ∑
−
=
−
=
=
1
0
1
0
),()()(
M
x
N
y
jqippqpq yxfyPxPL λ (3)
Where
( )( )
NM
qp
pq
×
++
=
1212
λ , xi and yj denote the normalized pixel coordinates in the range of
[-1, +1], which are given by:
( )
( )






−
−−
=
−
−−
=
1
12
1
12
N
Ny
y
M
Mx
x
j
i
(4)
( )xPp is the p
th
-order Legendre polynomial defined by:
( ) ( ) ( )
evenkp
p
k p
k
kp
p
kpkp
k
xkp
xP =−
=
−
∑

















 +





 −
+−
=
0
2
!
2
!
2
!2
!1 (5)
In order to increase the computation speed for calculating Legendre polynomials, we used the
recurrent formula of the Legendre polynomials defined by:
( ) ( ) ( ) ( ) ( )
( ) ( )




==
−
−
−
= −−
1,
112
01
21
xPxxP
xP
p
p
xP
p
xp
xP ppp
(6)

2.3 Texture Descriptors
This Several images have textured patterns. Therefore, the texture descriptor is used as feature
extraction method from the segmented image.
The texture descriptor is extracted using the co-occurrence matrix introduced by Haralick in 1973
[21]. So for a color image I of size 3×× NN in a color space ( )321 ,, CCC , for
( ) [ ]2
,,1, Nlk L∈ and ( ) [ ]2
,,1, Gba L∈ , the co-occurrence matrix [ ]IM CC
lk
',
, of the two color
components { }321 ,,', CCCCC ∈ from the image I is defined by:
[ ]( )
( )( )
( ) ( )( )∑∑
−
=
−
=
−++−
−−
=
kN
i
lN
j
CC
lk bCljkiIaCjiI
lNkN
baIM
1 1
',
, ',,,,,
1
,, δ (7)
Where δ is the unit pulse defined by:
( )


 ==
=
else
yxif
yx
0
01
,δ (8)
Each image I in a color space ( )321 ,, CCC can be characterized by six color co-occurrence
matrix:
[ ]IM CC 11 ,
, [ ]IM CC 22 ,
, [ ]IM CC 33 ,
, [ ]IM CC 21 ,
, [ ]IM CC 31 ,
, [ ]IM CC 32 ,
.
Matrix [ ]IM CC 12 ,
, [ ]IM CC 13 ,
and [ ]IM CC 23 ,
are not taken into account because they can be
deduced respectively by diagonal symmetry from matrix [ ]IM CC 21 ,
, [ ]IM CC 31 ,
and [ ]IM CC 32 ,
As they measure local interactions between pixels, they are sensitive to significant differences in
spatial resolution between the images. To reduce this sensitivity, it is necessary to normalize
these matrices by the total number of the considered co-occurrences matrix:
[ ]( )
[ ]( )
[ ]( )∑ ∑
−
=
−
=
= 1
0
1
0
',
,
',
,',
,
,,
,,
,, T
i
T
j
CC
lk
CC
lkCC
lk
jiIM
baIM
baIM (9)
Where T is the number of quantization levels of the color components
To reduce the large amount of information of these matrices, the 14 Haralick indices [21] of these
matrices are used. There will be then 84 textures attributes for six co-occurrence
matrices ( )614× .
3. NEURAL NETWORKS AND BAYESIAN NETWORKS CLASSIFIERS
3.1 Neural Networks
Neural networks (or artificial neural networks) learn by experience, generalize from previous
experiences to new ones, and can make decisions [22, 23].
A multilayer neural network consists of an input layer including a set of input nodes, one or more
hidden layers of nodes, and an output layer of nodes. Fig.1 shows an example of a three layer
network used in this paper, having input layer formed by M nodes, one hidden layer formed by L
nodes, and output layer formed by N nodes.

FIGURE 1: The Three Layer Neural Network.
This neural network is trained to classify inputs according to target classes. The training input
data are loaded from the reference database while the target data should consist of vectors of all
zero values except for a one element, where its index is the class they are to represent. The
transfer function used in this tree layer neural network is hyperbolic tangent sigmoid transfer
function defined by:
( )( ) 12exp12)( −−+= xxf (10)
According to authors in [24], the number of neurons in the hidden layer is approximately equal to:
( )( )21 ++= NMEL (11)
Where:
• E(x) denotes the integer part of x.
• M and N are respectively the number of neurons in the input and output layers.
3.2 Bayesian Networks
The Bayesian networks are based on a probabilistic approach governed by Bayes' rule. The
Bayesian approach is then based on the conditional probability that estimates the probability of
occurrence of an event assuming that another event is verified. A Bayesian network is a graphical
probabilistic model representing the random variable as a directed acyclic graph. It is defined by
[25]:
• ( )EXG ,= , Where X is the set of nodes and E is the set of edges, G is a Directed
Acyclic Graph (DAG) whose vertices are associated with a set of random variables
{ }nXXXX ,,, 21 L= ;
• ( )( ){ }ii XPaXP=θ is a conditional probabilities of each node iX relative to the state
of his parents ( )iXPa in G.
The graphical part of the Bayesian networks indicates the dependencies between variables and
gives a visual representation tool of knowledge more easily understandable by users. Bayesian
networks combine qualitative part that are graphs and a quantitative part representing the
conditional probabilities associated with each node of the graph with respect to parents [26].
Pearl and all [27] have also shown that Bayesian networks allow to compactly representing the
joint probability distribution over all the variables:
( ) ( ) ( )( )∏=
==
n
i
iin XPaXPXXXPXP
1
21 ,,, L (12)
L
Hidden Layer
*
w
b
+
N
Output Layer
*
w
b
+
Input
Output
M
Input Layer
*
w
b
+

Where ( )iXPa is the set of parents of node iX in the graph G of the Bayesian networks.
This joint probability could be actually simplified by the Bayes rule as follows [28]:
( ) ( ) ( )( )
( ) ( ) ( ) ( )
( ) ( )∏
∏
=
−
−−−
=
×=
××××=
==
n
i
ii
nnnn
n
i
iin
XXXPXP
XPXXPXXXPXXXP
XPaXPXXXPXP
2
111
11212111
1
21
,,
,,,,
,,,
L
LLL
L
(13)
The construction of a Bayesian network consists in finding a structure or a graph and estimates
its parameters by machine learning. In the case of the classification, the Bayesian network can
have a class node Ci and many attribute nodes jX . The naive Bayes classifier is used in this
paper due to its robustness and simplicity. The Fig 2 illustrates its graphical structure.
FIGURE 2: Naive Bayes Classifier Structure.
To estimate the Bayesian networks parameters and probabilities, Gaussian distributions are
generally used. The conditional distribution of a node relative to its parent is a Gaussian
distribution whose mean is a linear combination of the parent’s value and whose variance is
independent of the parent’s value [29]:
( )( ) ( )
























−+−−== ∑=
2
1
222 2
1
exp
2
1
jj
n
j j
ij
ii
ii
iii xxXPaxXP
i
µ
σ
σ
µ
σπσ
(14)
Where,
• ( )iXPa Are the parents of iX ;
• jiji and σσµµ ,, are respectively the means and variances of the attributes iX and
jX without considering their parents;
• in is the number of parents of iX ;
• jiσ is the regression matrix of weights.
After the parameter and structure learning of a Bayesian networks, The Bayesian inference is
used to calculate the probability of any variable in a probabilistic model from the observation of
one or more other variables. So, the chosen class Ci is the one that maximizes these probabilities
[30]:
Ci
XnX1 Xj… …

( )
( ) ( )( )
( ) ( )






=
∏
∏
=
=
.
.,
1
1
elseCXPCP
parentshasXifCXPaXPCP
XCP n
j
iji
j
n
j
ijji
i
(15)
For the naive Bayes classifier, the absence of parents and the variables independence
assumption are used to write the posterior probability of each class as given in the following
equation [31]:
( ) ( ) ( )∏=
=
n
j
ijii CXPCPXCP
1
(16)
Therefore, the decision rule d of an attribute X is given by:
( ) ( ) ( ) ( ) ( ) ( )∏=
===
n
j
iji
C
ii
C
i
C
CXPCPCPCXPXCPXd
iii 1
maxargmaxargmaxarg (17)
The class with maximum probability leads to the suitable keyword for the input image.
4. EXPERIMENTS AND RESULTS
After In this section, we study and compare the performance of discriminative and generative
classifiers for automatic image annotation using in first time each classifier alone and in second
time the combination of the two different classifiers [31].
In order to achieve this goal, we conduct two experiments on three image databases ETH-80
[32], COL-100 [33] and NATURE created in this work. The Fig.3 shows some examples of image
objects from these three image databases used in our experiments.
ETH-80
COIL-100

NATURE
FIGURE 3: Some objects images from ETH-80, COL-100 and NATURE databases.
In the phase of learning and classification, we used a training set of 40 images and a test set of
40 images for each image databases.
In all experiments, the features described in Section 2 are extracted after image segmentation by
region growing. For each region that represent an object, 10 components of Legendre moments
(L00, L01, L02, L03, L10, L11, L12, L20, L21, L30) and 16 elements for RGB color histograms
are extracted from each color plane namely R, G and B. The number of input features extracted
using Texture extraction method is 14 Haralick indices multiplied by 6 co-occurrence matrices.
This gives 84 textures attributes.
4.1 Experiment 1
In this experience, we provide comparative results of image annotation between the two
classifiers: discriminative (neural networks) and generative (Bayesian networks). The
experimental method adopted in this experience is represented by the figure 4.
In first time, we have used three neural networks classifiers to annotate images of all databases.
Each neural networks, receiving as input one of the three extracted descriptors, votes for a given
keyword. The keyword that has the maximum of votes is considered as the proper keyword for
the annotation of an object in a query image.
In second time, we repeated the same operation with Bayesian networks classifier as shown in
figure 4.
FIGURE 4: Experimental method adopted for image annotation.
Image
R
G
B
Shape
Descriptor:
[VShape]
Texture
Descriptor:
[VTexture]
Color
Descriptor:
[VColor]
Vote
and
Decision
Annotation
Result
Image
Segmentation
Classifier
Classifier
Classifier

4.1.1 Results
Table I summarizes the results of automatic image annotation for each type of classifier and
Figures 5,6,7,8, 9 and 10 shows the confusion matrix.
Database Classification Approach Average Annotation Rate Error Rate
ETH-80
neural networks 87.50% 12.50%
Bayesian networks 90.00% 10.00%
COIL-100
NATURE
TABLE 1: Average annotation rate and error rate.
FIGURE 5: Confusion matrix for images of database ETH-80 by using Bayesian networks.
FIGURE 6: Confusion matrix for images of database ETH-80 by using neural networks.

FIGURE 7: Confusion matrix for images of database NATURE by using Bayesian networks.
FIGURE 8: Confusion matrix for images of database NATURE by using neural networks.

FIGURE 9: Confusion matrix for images of database COIL-100 by using Bayesian networks.

FIGURE 10: Confusion matrix for images of database COIL-100 by using neural networks.
4.2 Analysis of Results
As can be observed from Table 1, Bayesian networks produce the better average annotation
rates for all the tree images databases. However, analysis of confusion matrix presented by the
Figures 5,6,7,8, 9, 10 shows that the individual annotation rate obtained for some objects (cow,
cup, object 6, Sahara and Gazon) with neural networks can be better than those obtained with
Bayesian networks. So it appears from these remarks that the combination of these two
classifiers will improve the average annotation rates. This constitutes the aim of the experiment 2.
4.3 Experiment 2
Based on the remarks released in the previous two experiments, we combined in this experiment,
in addition to descriptors, neural networks and Bayesian networks in order to gain the benefit of
the complementarity of these two approaches of classification (discriminative and generative).
The principle of this combination is illustrated by the block diagram shown in Fig 11. Thus, with
the combination of the three types of descriptors described in Section 2 and the 2 considered
types of classifiers, there will be a maximum of votes equal to 3 x 2 = 6. Each classifier with each
descriptor votes for a given keyword. The keyword with a maximum of votes will be deemed as
the proper keyword for the annotation of an object contained in a query image.

FIGURE 11: Block diagram that illustrates principle of combining discriminative and generative
classifiers for automatic image annotation.
4.4 Results
Table 2 shows the average image annotation rate obtained by combining neural networks and
Bayesian network classifiers and Figures 12, 13 and 14 shows the confusion matrix.
Database Average Annotation Rate Error Rate
ETH-80 92.50% 7.50%
COIL-100 87.50% 12.50%
NATURE 96.67% 3.33%
TABLE 2: Average annotation rate and error rate.
FIGURE 12: Confusion matrix for images of database ETH-80.
Image
R
G
B
Shape
Descriptor:
[VShape]
Texture
Descriptor:
[VTexture]
Color
Descriptor:
[VColor]
Vote and
decision
Annotation
Result
Image
Segmentation
Neural
network
Bayesian
network
Neural
network
Bayesian
network
Neural
network
Bayesian
network

FIGURE 13: Confusion matrix for images of database NATURE.
FIGURE 14: Confusion matrix for images of database COIL-100.

4.5 Analysis of Results
Analysis of the results presented in Table 2, Figures 12, 13 and 14, allows us to notice that the
combination of neural networks with Bayesian networks in a parallel scheme, has significantly
improved the quality of image annotation. Although, some errors are still persistent, namely in
particular, the confusion between car and Cow in some times. This result is also illustrated by the
examples of annotated images presented by figures 15 and 16 which shows that the exploitation
of complementarities of generative and discriminative classifiers can contributes to the
improvement of the image annotation. So, it would be interesting to investigate other ways to
combine these two different classification approaches to possibly correct the observed annotation
errors.

FIGURE 15: Examples of annotated images.

FIGURE 16: Examples of annotated images
5. CONCLUSION AND FUTURE WORK
In this work, we have proposed to build an efficient classifier for automatic image annotation via
combining generative and discriminative classifiers which are respectively Bayesian networks and
neural networks.
Starting with comparing these classifiers by realizing experiments on three image dataset, we
have observed that neither classifier alone will be sufficient for semantic image annotation. So,
we have combined the generative and discriminative classifier in parallel scheme in order to join
and exploit their strengths. Experimental results show that this approach is promising for
automatic image annotation because it gives better classiﬁcation accuracy than either Bayesian
networks or neural networks alone.
Our investigations suggest that the most fruitful approaches will involve some combination of
generative and discriminative models. A principled approach to combining generative and
discriminative approaches not only gives a more satisfying foundation for the development of new

models, but it also brings practical benefits, address the extreme data-ambiguity and overﬁtting
vulnerability issues in tasks such as automatic image annotation (AIA). In future work, we would
like to develop others hybrid schemes that sought to integrate the intra-class information from
generative models and the complementary inter-class information from discriminative models,
and to research alternative optimization techniques utilizing ideas from the multi-criteria
optimization of literature.
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