Journ al of Software Engineering and Applications, 2012, 5, 200-203
doi:10.4236 /j sea.2 012.512b038 Published Online December 2012 (
Copyright © 2012 SciR es. JSEA
Image Classification using Statistical Learning Methods
Jassem Mtimet, Hamid Amiri
Signal, Image and Technology of Information Laboratory, National Engineering School of Tunis, Tunis El Manar University, BP 37,
Le Belvdre 1002, Tunis, Tunisia.
Email: mtimat. jasse m@y a hoo .fr,
Received 2012
In gene ral, d igital i mages can b e classi fied into photo grap hs, text ual and mixed d ocu me nts. T his taxo nomy is very use-
ful in many applications, such as archiving task. However, there are no effective methods to perfor m this classification
automatically. In this paper, we present a method for classifying and archiving document into the following semantic
classes: photographs, textual and mixed documents. Our method is based on combining low-level i mage fea tures, s uch
as mean, Standard deviation, Skewness. Both the Decisio n Tree and Neuronal Network Classifiers are used for classifi-
cation task.
Keywords: Image Classification; Decision Tree; Neuronal Network; Statistica l Ana l ysi s
1. Introduction
Nowadays, a huge number of documents are available in
electronic format, whether as photos, p lans, letter s or pres s
releases. With the continuous increase of the amount of
such information, many applications for organizing this
flood of documents are emerging. Amongst them, auto-
matic image archiving systems are necessary to classify
and to store a large collection of documents autono-
mously, to simplify searching and retrieving individual
Recently automatic semantic classification and arc-
hiving of images has become an important field of re-
search, aiming to automatically classify images, i.e. clas-
sification of images into significant categories, such as
outdoor/indoor, city/landscape and people/non-people
scene s [1,2].
In order to classify images into two classes (in-
door/outdoor, city/landscape, etc.) Vailaya et al. use a
Bayesian framework and obtain an average accuracy of
94.1% [3].
In [4] Gorkani et al. suggest an image classification
method based on the most dominant orientation in the
image’s texture. In fact, this feature a llows dif fere ntiating
two final classes of images: city and landscape. Thus,
they achieve a classification accuracy of 92.8%.
Another approach was proposed by Prabhakar et al. in
[5]. They used three low-level image descriptors (color,
texture and edge information) to separate pictures and
graphic images. Their algorithm reaches an accuracy rate
of 96.6%.
In [6] Schettini et al. aim to classify images into four
classes (photographs, graphics, text and mixed docu-
ments). Therefore, from every image, they extract six
features which represent color descriptor, edge represen-
tation, texture features, wavelet coefficients and skin
color pixels percentage.
This paper presents a system able to automatically
classify a nd ar chivin g d oc u me nts i nto the fo ll o win g t hre e
categories: photos, textual documents and mixed docu-
In Section 2, theoretic background of our approach is
explained. Then in section 3, the experience plan is de-
scribed, including data sets, experimental results and
evaluation criteria, while in Section 4, results are dis-
cussed and new perspectives are suggested.
2. Proposed System
The system we propose allows discriminating documents
into photographs, textual and mixed documents. It is
based on two main stages (F ig ure 1): i) The features
extraction: These features are extracted automatically
from images using specific programs. For every single
image, the values of these features will be used as coeffi-
cients of a representative vector. ii) The classification
and archiving mo dule : This i s o btaine d after trai ning a nd
validating a model used to discriminate and store docu-
2.1. Features Extraction
Features selection is the key step leading to the success
or failure of the classification phase. Therefore, several
Image Classification using Statistical Learn ing Methods
Copyright © 2012 SciR es. JSEA
features are tested, looking to their relevance. In fact,
features selection is an empiric process, though many
approaches are suggested to weight their importance. In
our system, images are classified based on six low-level
featured, these features are considered as the coefficients
of the image representative vector. They are calculated as
Mean: is the average color value in the image.
i ij
µP N=
= × (1)
Were i represent the color channel and Pij is the pr ob-
ability of occurrence of pixel wit h intensity j.
Standard deviation: is the square root of the va-
riance of the distr ibution
( )
iij i
= −
Skewn e ss: represents the measure of the degree of
asymmetr y in t he distribution.
( )
iij i
= −
Entropy: represent the disorder or the complexity
of the image. A high value of entropy indicates a
complex textur es.
log log
i ijij
= −
Image dimension: represents the length and width
of the image.
2.2. Classification Stage
After the extraction of the representative vector for each
image, every document is classified as a photo, text or a
mixed one. Photo family included indoor, outdoor,
Doc uments
Extraction of
image features
Training the
model validation
Classified Images
Figure 1. Impleme ntation strategy.
scenes, landscape, people, logos, and maps. Text family
includes scanned and computer-generated text in various
fonts. Mixed documents are documents that contain text
and photo region.
Thus, two well known classifiers are used to classify
our doc ument s na mel y the De cisio n tre e a nd the Ne uron-
al Network [7,8].
The Decision Trees
The Decision Tree Classifier is a set of hierarchical
rules which are successively applied to the input data [9].
Those rules are thresholds used to split the data into two
binary nodes. Each node is such that the descendant
nodes contain more homogeneous data samples. Many
features can be input into the Decision Tree to refine
class description. A split is chosen because of its ability
to render the nodes purer based on a purity measure and
can be determined by any single feature [10].
In our paper we fitted the DT to the training data using
the cross validation technique in order to select the best
tree. Thus, we obtained two tree-based models (original,
pruned) that we re used in the classification task.
The Artificial Neuronal Network
A neural network is a set of connected units (nodes,
neurons). Each node has an input and output then it can
be connects with other nodes. Each connection has a
weight associated to it. The topology of the neural net-
work, the training methodology and the connections be-
tween the different nodes define the type of the corres-
ponding Ne uronal Network [11-13]. In our case we used
an RBF network. In which the input layer had 6 nodes
that are equal to the number of features organized as
vectors in the database. For the hidden layer, we chose 6
node s while the outp ut l ayer co ntain s thre e node s. B y the
end of this process, an input image is classified either as
a photo, a pure text or a compound documen t.
3. Experimental Results
A data base of 291 documents was considered for both
classification systems. From this set of documents 75%
were used for training and 25% for testing the system
performance. Thus, the training data set consists of 136
photo including indoor, outdoor, scenes, landscape im-
ages documents, 39 textual documents include scanned
and computer-generated text in various font and 51
compound documents. Figure 2 shows some of the class
images from the training data set.
In order to evaluate the accuracy of our approach, the
following statistical coef ficients are c omputed [14 ][15]:
The recall rate= CCI/TI
The precision rate= CCI/(TI+MI)
( )
( )
1Precision Recall
Precision Recall
+⋅ ⋅
. Here, b
Image Classification us in g Statistical Learning Met hod s
Copyright © 2012 SciR es. JSEA
equals 1.
CCI represents the number of Correctly Classified
Images. MI is the number of Misclassified Images and TI
is the number of Test Images for each class.
Figure 3 presents the results obtained by using the
Decision Tree. We can see that only for textual docu-
ments the full Decision Tree achieve high F-measure
value than the p rune d one .
The results obtained using the neural network as clas-
sifier are presented in Fig ure 4. These results show that
both classifiers achieve notable results in the classifica-
tion of documents. The DT classifier outperforms the NN
classifier in execution speed and Recall value (by 12%).
There are some cases of misclassification produced by
the both classifiers. Figure 5 shows examples of these
The main causes of misclassification on text are due to
bad lighting conditions and to excessively noisy back-
grounds that cause the final unifor mity te st to fail.
Figure 2 . Examples of training data set images.
Figure 3. Classification results using DT.
Figure 4. Classification results using NN.
Figure 5 . Samples of mis classifi ed images.
4. Conclusions
Automatic classification and archiving of images is an
emerging research field in image processing. In this pa-
per an algor ithm for cla ssifyi ng phot o, text ual a nd mixe d
documents based on low-level image features was pre-
sented. Firstly, features are extracted from images to be
assigned to a characteristic vector. Then, the Decision
Tree and the neuronal Network classifiers are used to
train and to validate a classification model using the ex-
tracted feature vectors. The obtained models allowed
reaching an accuracy rate of 96% for discriminating a
photo, a text and a mixed document.
Nevertheless, features relevance is weighted to select
the most contributory ones, in order to increase classifi-
cation and archiving performance. Moreover, we are
curr ently stud ying othe r usefu l high-level feature to raise
the accuracy and to build a new intelligent classifier.
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