Ensemble Image Classification Method Based on
Genetic Image Network
Shiro Nakayama, Shinichi Shirakawa, Noriko Yata and Tomoharu Nagao
Graduate School of Environment and Information Sciences, Yokohama National
University, 79-7, Tokiwadai, Hodogaya-ku, Yokohama, Kanagawa, 240-8501, Japan
shiro@nlab.sogo1.ynu.ac.jp, shirakawa@nlab.sogo1.ynu.ac.jp,
yata@nlab.sogo1.ynu.ac.jp, nagao@ynu.ac.jp

Abstract. Automatic construction method for image classification algorithms have been required. Genetic Image Network for Image Classification (GIN-IC) is one of the methods that construct image classification
algorithms automatically, and its eﬀectiveness has already been proven.
In our study, we try to improve the performance of GIN-IC with AdaBoost algorithm using GIN-IC as weak classifiers to complement witheach other. We apply our proposed method to three types of image classification problems, and show the results in this paper. In our method,
discrimination rates for training images and test images improved in theexperiments compared with the previous method GIN-IC.

1

Introduction

Automatic construction method for image classification algorithms have been required. In general, image classification algorithms consist of image preprocessing,feature extraction, and classification process. It is very diﬃcult to construct analgorithm suitable for all image classification problems. Therefore, a method isrequired to construct an image classification algorithm that would automaticallyadjust to the target problem is needed. Genetic Image Network for Image Classification (GIN-IC) [1] is one of the methods that construct image classification
algorithms automatically, and its eﬀectiveness has already been proven. GIN-IC
automatically constructs the adequate classification algorithm (including imagetransformation, feature extraction and arithmetic operation components) using
evolutionary computation. The process of GIN-IC is, first, to transform original images to easier-to-classify images using image transformation nodes, andnext, to select adequate image features using feature extraction nodes. The greatest advantage of GIN-IC is its image transformation (preprocessing) component,
which influences image feature selection. However, learning failure or over fittingof the training images sometimes occurs in the constructed algorithms becauseof GIN-IC’s simple output to decide the classification.
In this paper, we extend GIN-IC by adding the AdaBoost algorithm [2].
AdaBoost is one of the greatest general ensemble learning methods to make astrong classifier, which has higher performance, by combining weak classifiers,

Fig. 1. Example of a structure in Genetic Image Network for Image Classification.

which have lower performance. AdaBoost is applied to various image classification problems and has shown its eﬀectiveness, such as the face detection
method proposed by Viola and Jones [3]. Moreover, aggregating the classifiers
constructed by genetic programming [4–6], particle swarm optimization [7] orneural networks [8] has been studied to improve their classification performance.
In our method, a set of output nodes in GIN-IC is treated as a weak classifier.
The performance is expected to be improved by using GIN-IC as weak classifiers
that complement with each other.
The next section of this paper is an overview of GIN-IC. In section 3, we
describe our proposed method. In section 4, we apply the proposed method tothree kinds of image classification problems and show their results in section 5.
Finally, in section 6, we describe conclusions and future work.

2

Genetic Image Network for Image Classification(GIN-IC)

2.1

Structure of GIN-IC

The image classifier GIN-IC consists of image transformation, feature extraction,
and arithmetic operation components based on the Genetic Image Network [9].
GIN-IC constructs an acyclic network-structured image classifier automatically.
Fig. 1 shows an example of the phenotype (feed-forward network structure) andgenotype (string representing the phenotype) of GIN-IC.
One of the benefits of this type of representation is that it allows the implicit reuse of nodes in its network. The nodes of GIN-IC are categorized into
five types: input nodes, image transformation nodes, feature extraction nodes,

arithmetic operation nodes, and output nodes. Input nodes correspond to theoriginal images. Image transformation nodes execute image transformation using
the corresponding well-known image processing filters. Feature extraction nodes
extract an image feature from the input images. Arithmetic operation nodes execute arithmetic operations. Image classification is performed using the valuesof the output nodes. In GIN-IC, these processes evolve simultaneously.
In GIN-IC, the feed-forward network structure of nodes is evolved, as shownin Fig. 1. Numbers are allocated to each node, beforehand. Increasingly large
numbers are allocated, in order, to the input nodes, image transformation nodes,
feature extraction nodes, arithmetic operation nodes, and output nodes. Connections, such as the feedback structure, that cannot be executed are restricted
at the genotype level. The nodes take their input from the output of the previous
nodes in a feed-forward manner. Because GIN-IC constructs a feed-forward network structured image classification procedure, it can represent multiple outputs.
Therefore, GIN-IC enables easy construction of a multiclass image classification
procedure using a single network structure.
To adopt an evolutionary method, GIN-IC uses genotype-phenotype mapping. This genotype-phenotype mapping method is similar to Cartesian Genetic
Programming (CGP) [10]. The feed-forward network structure is encoded in theform of a linear string. The genotype in GIN-IC is a fixed length representationand consists of a string that encodes the node function ID and connections ofeach node in the network. However, the number of nodes in the phenotype canvary in a restricted manner, as not all the nodes encoded in the genotype haveto be connected. This allows the existence of inactive nodes. In Fig. 1, node No.
8 and 11 are inactive nodes.
2.2

Genetic Operator and Generation Alternation Model

To obtain the optimum structure, an evolutionary method is adopted. The genotype of GIN-IC is a linear string. Therefore, it is able to use a standard genetic
operator. In GIN-IC, mutation is used as the genetic operator. The mutation
operator aﬀects one individual, as follows:– Select several genes randomly according to the mutation rate Pm for eachgene.
– Randomly change the selected genes under the structural constraints.
(1 + 4) Evolution Strategy ((1 + 4) ES) is used as the generation alternation
model. The (1 + 4) ES procedure in the experiments works as follows:
1. Set generation counter j = 0. Generate an individual randomly as a parent
M.
2. Generate a set of four oﬀspring C, by applying the mutation operation toM.
3. Select the elite individual from the set M + C (the oﬀspring is selected if
it has the same best fitness as the parent). Then replace M with the elite
individuals.

4. Stop if a certain specified condition is satisfied; otherwise, set j = j + 1 andgo to step 2.
Since GIN-IC has inactive nodes, a neutral eﬀect on fitness is caused bygenetic operation (called neutrality [10]). In step 3, the oﬀspring is selected if
it has the same best fitness as the parent, then the searching point moves even
if the fitness is not improved. Therefore, eﬃcient search is achieved though asimple generation alternation model. This (1 + 4) ES was adopted and showed
its eﬀectiveness in previous works [1, 10].
2.3

Advantages and Limitations of GIN-IC

GIN-IC has many advantages to automatically construct image classification
algorithms. The greatest advantage of GIN-IC is its image transformation (preprocessing) component, which is expected to influence image feature selection.
In other words, GIN-IC generates and selects adequate image features by a combination of nodes. However, learning failure or over fitting of the training images
sometimes occurs in the constructed algorithms because of GIN-IC’s simple output to decide the classification. Moreover, more the size or the number of training
images is increased, more time for learning it takes.

3

Proposed Method

3.1

Overview

To solve the problems noted in previous section, we apply the AdaBoost [2]algorithm to GIN-IC. A set of output nodes in GIN-IC is treated as a weakclassifier. Thus, the total number of output nodes is N × T , where N is thenumber of classes and T is the number of weak classifiers. Weak classifiers are
evolved in sequence until each weak classifier achieves a specified error rate. In
this process, the construction of the previous weak classifiers is fixed and canbe reused in the subsequent weak classifiers. The eﬀective process of other weakclassifiers can be reused. Moreover, reuse is expected to reduce the time required
for learning by avoiding recalculation at each operational node. In addition,
all weak classifiers have a weight of their hypothesis. The final hypothesis is aweighted vote of the hypothesis of all weak classifiers. Fig. 2 shows an example
of a structure in our proposed method for binary classification.
3.2

Process of the Proposed Method

The process of our proposed method is described as follows:1
Step 1: Initialize the weights D1 (i) = m, (i = 1, 2, . . . , m), and weak classifiercounter t = 1, where m is the number of training images.

Fig. 2. Example of a structure in proposed method for binary classification.

Step 2: Focus the tth output set as the tth weak classifier. Here, add the newusable nodes to save enough nodes for constructing the new weak classifier.
Add ni of image transformation nodes, nf of feature extraction nodes andna of arithmetic operation nodes.
Step 3: Evolve the weak classifier based on GIN-IC till the error rate t is lessthan a threshold τ . t is obtained by summing weights Dt (i) corresponding
to misclassified images as∑
Dt (i),(1)t =i:ht (xi )6=yi

where ht (xi ) is the class label corresponding to the output node with thelargest value of the weak classifier t when the input is image xi , and yi is thecorrect class label of image xi . Here, we use (1 + 4) ES as the generation
alternation model. The (1 + 4) ES procedure is the same as in section 2.2.
Do not operate the fixed nodes in step 6 by genetic operation, and allow
these nodes to be selected as the input of other nodes. When constructing
the tth classifier, the structures of the classifiers from first to (t − 1)th canbe reused.
Step 4: Calculate the weight of hypothesis α of the focused output set as()
1
1 − tαt = log.
(2)2
t
Step 5: Update the weights as
Dt+1 (i) =Dt (i)
×Zt{

exp (−αt ) if ht (xi ) = yi,
exp (αt )otherwise(3)

where Zt is the number for normalization. This operation adapts the weights
corresponding to the training images based on the AdaBoost algorithm. By
this operation, the weights corresponding to the training images classified
correctly are decreased and the weights corresponding the training images
misclassified by the previous weak classifiers are increased. Therefore, themisclassified images are classified correctly by the next weak classifier preferentially.
Step 6: Set t = t + 1. If t ≤ T , fix the nodes connected with the focused output
set and go back to step 2.
Step 7: Output the final hypothesis hfin (xi ) as∑
hfin (xi ) = arg max
αt .
(4)
yi ∈Y

t:ht (xi )=yi

This operation calculate the final classification results by a vote of all weakclassifiers. For all weak classifiers, sum the weights of hypothesis αt for theclass output by tth weak classifier. The input image xi is classified the classhfin (xi ), which maximizes the sum of the weights of weak classifiers that
output the class.
3.3

Characteristics of the Proposed Method

We note some characteristics of our proposed method in this section. First, theclassifier constructed by our proposed method consists of a number of GIN-IC
as weak classifiers. We think that the performance is improved because weakclassifiers complement with each other compared with GIN-IC. Second, the construction of the previous weak classifiers is fixed and can be reused in the subsequent weak classifiers. Therefore, the eﬀective process of other weak classifiers
can be reused since all nodes may be selected as the input of other nodes. Third,
we stop evolving GIN-IC on the way to use it as weak classifiers. This operation
is expected to reduce the time required for learning and to prevent over fitting.

4

Experiments

4.1

Settings of the Experiment

In this section, we evaluate our proposed method by applying it to three image
classification problems. The problems are as follows:
1. Texture images
2. Pedestrian images
3. Generic object images
In these experiments, we transform all color images into grayscale. We also apply
the previous method, GIN-IC, to the same problems to compare it with our
proposed method. Table 1 shows the parameters used in the proposed method

Table 1. Parameters used in the experiments.
Parameters
GIN-IC Proposal
Generation alternation model(1+4)ES (1+4)ES
Mutation rate (Pm )
0.02
0.02
Image transformation nodes (ni )100
Add 50Feature extraction nodes (nf )100
Add 50Arithmetic operation nodes (na )100
Add 50
Output nodes
6, 2, 5 6, 2, 5112500–The number of generations
The number of weak classifiers (T )–100
Error rate threshold (τ )–0.4

and the previous method, GIN-IC. We determined each parameter by the resultsof preliminary experiments.
We prepare simple and well-known image processing filters as the imagetransformation nodes in the experiments (26 one-input, one-output filters and 9
two-input, one-output filters), e.g., mean filter, maximum filter, minimum filter,
Sobel filter, Laplacian filter, gamma correction filter, binarization, linear transformation, diﬀerence, logical sum, logical prod, etc. 17 simple statistical values
are used as feature extraction nodes, e.g., mean value, standard deviation, maximum value, minimum value, mode, 3 sigma in rate, 3 sigma out rate, skewness,
kurtosis, etc. The arithmetic operation nodes are 20 well-known arithmetic operations, e.g., addition, subtraction, multiplication, division, threshold function,
piecewise linear function, sigmoid function, absolute value, equalities, inequalities, constant value, etc. In both the proposed method and previous method(GIN-IC), we use the same kinds of nodes. The fitness function of GIN-IC as
weak classifiers in proposed method is described as follows:
fitness = Nc × m +

1
,
Na

(5)

where Nc is sum of the weights Dt (i) of training images classified correctly, Na
is the number of active nodes, and m is the number of training images. If theclassification performance is the same, the number of active nodes should besmall in this fitness function. We describe the features of the training images
used in each experiment as follows:
Experiment 1: Texture Images We use texture images from the publicly
available database VisTex.1 We use six classes in this experiment. We make 128
images with 64 × 64 pixels each by dividing two texture images of 512 × 512
pixels for each class. The number of training images is 60 (10 images for eachclass). The main feature of these images is that the test images are comparatively
1

http://vismod.media.mit.edu/vismod/imagery/VisionTexture/vistex.html

Fig. 3. Training images used in experiment 1 (texture).

Fig. 4. Example images used in experiment 2 (pedestrian).

similar to the training images. The training images used in this experiment are
displayed in Fig. 3.
Experiment 2: Pedestrian Images We use 924 pedestrian images from thepublicly available database MIT Pedestrian Database2 and 200 nonpedestrian
images. We use two classes in this experiment. The size of all images is 64 × 128
pixels, and the number of the training images is 200 (100 images for each class).
The pedestrian images have various resolutions while the pedestrians are roughly
the same size. The nonpedestrian images are manually cut out from outdoor
images. An example of the training images used in this experiment is displayedin Fig. 4.
Experiment 3: Generic Object Images We use 500 generic object images
from the publicly available WANG image database.3 We use five classes in thisexperiment. The size of all images is 96 × 64 or 64 × 96 pixels, and the number of
23

http://cbcl.mit.edu/software-datasets/PedestrianData.html
http://wang.ist.psu.edu/docs/related

Fig. 5. Training images used in experiment 3 (generic object).

Table 2. Discrimination rate for the training and test images (texture).
Training set
Test set
Class GIN-IC Proposal
GIN-IC
Proposal
Average Average Average ± SD Average ± SD
Bark 99.0% 100.0% 70.7 ± 9.2% 91.7 ± 4.0%
Food 100.0% 100.0% 86.1 ± 5.8% 98.4 ± 1.8%
Grass 100.0% 100.0% 88.1 ± 3.2% 85.6 ± 6.5%
Metal 90.0% 100.0% 76.7 ± 16.2% 98.3 ± 1.9%
Stone 99.0% 100.0% 80.4 ± 10.5% 94.9 ± 5.7%
Fabric 100.0% 100.0% 94.7 ± 6.1% 99.4 ± 0.8%
Total 98.0% 100.0% 82.8 ± 8.2% 94.7 ± 1.5%

training images is 50 (10 images for each class). The main feature of these images
is that target objects have various sizes, positions, types, and so on. Therefore,
this problem is more diﬃcult than the other two problems. The training images
used in this experiment are displayed in Fig. 5.

5Results and Discussion

5.1

Results

We compare our proposed method with GIN-IC in discrimination rate for training and test images and time required for learning in this section. The results
are the average and the standard deviation (SD) over 10 diﬀerent runs.
As the result of experiment 1, discrimination rates for the training and test
images of textures are shown in Table 2. Our proposed method achieved 100%classification accuracy for all training runs against 98% on an average in GIN-IC.

Table 3. Discrimination rate for the training and test images (pedestrian).
Training set
Test set
GIN-IC Proposal
GIN-IC
Proposal
Average Average Average ± SD Average ± SD
Pedestrian
89.5% 100.0% 81.4 ± 7.0% 88.6 ± 4.2%
Non-pedestrian 90.5% 100.0% 73.0 ± 10.5% 75.7 ± 4.0%
Total
90.1% 100.0% 80.5 ± 6.1% 87.2 ± 3.7%
Class

Table 4. Discrimination rate for the training and test images (generic object).
Training set
Test set
GIN-IC Proposal
GIN-IC
Proposal
Average Average Average ± SD Average ± SD
Building 76.0% 100.0% 34.3 ± 19.5% 62.7 ± 6.9%
Bus
95.0% 100.0% 67.6 ± 12.4% 74.8 ± 7.8%
Elephant 95.0% 100.0% 66.3 ± 10.7% 84.0 ± 5.9%
Flower100.0% 100.0% 70.9 ± 9.1% 86.4 ± 7.3%
96.0% 100.0% 56.6 ± 19.5% 73.4 ± 7.9%
Horse
Total
92.4% 100.0% 59.1 ± 6.3% 76.3 ± 3.5%
Class

Moreover, the proposed method is about three times faster than GIN-IC, for the
training images. For test images, the proposed method also obtained about 12%
higher classification accuracy as compared to that in GIN-IC totally.
As the result of experiment 2, Table 3 shows discrimination rates for the
training and test images of pedestrians. As for the results of experiment 1, theproposed method achieved 100% accuracy for all training runs against 90% on
an average in GIN-IC. Moreover, the proposed method is about 50 times fasterthan GIN-IC, for the training images. Our proposed method also obtained higher
classification accuracy for test images as compared to GIN-IC totally.
As the result of experiment 3, discrimination rates for the training and test
images of generic objects are shown in Table 4. Similar to the results of experiments 1 and 2, the proposed method achieved 100% classification accuracy for
all training runs against about 92% on an average in GIN-IC. Moreover, the proposed method is about two times faster than GIN-IC, for the training images.
The proposed method also obtained about 20% higher classification accuracy
than GIN-IC totally, for test images.
5.2

Discussion

Our proposed method classified all training images completely in all runs andtends to prevent over fitting as compared to single GIN-IC in these experiments.
We confirmed that GIN-IC and AdaBoost go well together. Since we use GINIC as weak classifiers, our proposed method can generate and select adequate

Fig. 6. Example of discrimination ratetransition in GIN-IC.

Fig. 7. Relationship between the number of weak classifiers and the error rate
threshold τ .

image features by a combination of nodes. Although we only use simple image
processing filters and image features as nodes in these experiments, we think thatthe performance is improved by adding more complex and eﬀective processes
such as SIFT descriptor [11]. We should investigate how GIN-IC contributes theperformance of our proposed method compared with low level features.
Moreover, our proposed method took lesser time than single GIN-IC, forlearning. We attribute this superiority to using GIN-IC as weak classifiers. An
example of discrimination rate transition in experiment 2 (pedestrian) is shownin Fig. 6. About 60% classification accuracy is achieved in dozens of generations
and higher accuracy costs hundreds and thousands generations. This graph indicates that error rate threshold τ should be small to reduce the number ofgenerations to construct weak classifiers. However, too small τ brings a large
number of weak classifiers. The relationship between the number of weak classifiers and τ in pedestrian datasets is shown in Fig. 7. The vertical axis indicatesthe number of weak classifiers needed to completely classify the training images,
and the horizontal axis indicates τ . This graph indicates that many weak classifiers are required if τ is small. From this preliminary experiment, we decide theparameters of τ and T .

6Conclusions and Future WorkIn this paper, we propose a method for automatic construction of an image
classifier that aggregates GIN-IC as weak classifiers based on the AdaBoost algorithm. We applied the proposed method to three diﬀerent problems of image
classification, and confirmed that it obtained the optimum solution. In our proposed method, discrimination rates for the training and test images improved in
the experiments as compared to that in the previous method GIN-IC. Also, ourproposed method reduces the time required for learning.

However, the classifier obtained by the proposed method is constructed with
2500 nodes under 100 weak classifiers while single GIN-IC is constructed with
30 nodes. We will analyze the process of obtaining classifiers and what kindof preprocessing and features were evolved by our proposed method in future.
Moreover, this method should be compared with other classifiers to evaluate its
quality. Finally, we will apply it to other problems of image classification andobject recognition with large scale variation and so on.

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