Genetic Image Network for Image Classification
Shinichi Shirakawa, Shiro Nakayama, and Tomoharu Nagao
Graduate School of Environment and Information Sciences, Yokohama National
University, 79-7, Tokiwadai, Hodogaya-ku, Yokohama, Kanagawa, 240-8501, Japanshirakawa@nlab.sogo1.ynu.ac.jp, shiro@nlab.sogo1.ynu.ac.jp,
nagao@ynu.ac.jp

Abstract. Automatic construction methods for image processing proposed till date approximate adequate image transformation from original
images to their target images using a combination of several known image
processing filters by evolutionary computation techniques. Genetic Image Network (GIN) is a recent automatic construction method for image
processing. The representation of GIN is a network structure. In this paper, we propose a method of automatic construction of image classifiers
based on GIN, designated as Genetic Image Network for Image Classification (GIN-IC). The representation of GIN-IC is a feed-forward network
structure. GIN-IC transforms original images to easier-to-classify images
using image transformation nodes, and selects adequate image featuresusing feature extraction nodes. We apply GIN-IC to test problems involving multi-class categorization of texture images, and show that theuse of image transformation nodes is effective for image classification
problems.

1

Introduction

Various image processing and recognition techniques using evolutionary computation (EC) have been studied and their effectiveness is demonstrated [1]. The
typical examples are template matching, image filter design, image segmentation,and image classification. In particular, genetic programming (GP) [2] has beenfrequently applied to image classification tasks [3–6]. Tackett primarily applied
GP to solve image classification problems [3]. Parallel Algorithm Discovery andOrchestration (PADO) [4, 5], a graph-based GP method, was applied to object
recognition problems. Zhang et al. used linear genetic programming (LGP) for
multi-class image classification [6]. The results showed that this approach outperforms the basic tree-based GP approach. In addition, GP was used to develop
useful texture-feature extraction algorithms [7, 8]. In these studies, the evolved
features are either at par or outperform human-designed features.
In addition, automatic construction methods for image processing have been
proposed [9–13]. They have constructed various types of image processing algorithms automatically. The genetic image network (GIN) is a recent automaticconstruction method for image processing [12, 13]. The representation of GIN isa network structure.

In general, it is difficult to select and extract image features because theappropriate image features depend on the images of a target problem. In thispaper, we propose a method for automatic construction of image classifiers basedon GIN, the Genetic Image Network for Image Classification (GIN-IC). The representation of GIN-IC is a feed-forward network structure. GIN-IC is composedof image transformation nodes, feature extraction nodes, and arithmetic operation nodes. GIN-IC transforms the input images to easier-to-classify images
using the image transformation nodes and selects adequate image features using
the feature extraction nodes. We apply GIN-IC to test problems of multi-classclassification of texture images.
The next section of this paper is an overview of several related studies. In
Section 3, we describe our proposed method, GIN-IC. Next, in Section 4, weapply the proposed method to the problem of texture classification and show
several experimental results. Finally, in Section 5, we describe conclusions andfuture work.

2

Related Work

2.1

Genetic Programming and Graph-based Genetic Programming

Genetic Programming (GP) [2], an evolutionary computation technique, wasintroduced by Koza. GP evolves computer programs, which are usually tree
structures, and searches for a desired program using a genetic algorithm (GA).
Recently, many extensions and improvements to GP have been proposed. Parallel Algorithm Discovery and Orchestration (PADO) [4, 5] is a graph-based GP
rather than a tree structure. PADO was applied to object recognition problems.
Cartesian genetic programming (CGP) [14] was developed from a representation
used for the evolution of digital circuits and it represents a program as a graph.
In CGP, the genotype is an integer string that denotes a list of node connections
and functions. This string is mapped into the phenotype of an index graph.
2.2

Automatic Construction of Image Transformation

In image processing, it is difficult to select filters satisfying the transformation from original images to their target images. The Automatic Construction ofTree structural Image Transformation (ACTIT) system [9–11] has been proposed
previously. ACTIT approximates adequate image transformation from original
images to their target images by a combination of several known image processing filters. ACTIT constructs tree-structured image processing filters using
GP [2]. The individual in ACTIT is a tree-structured image transformation.
The terminal nodes of a tree are the original images, whereas the non-terminal
nodes are several kinds of image processing filters. A root node represents an
output image. Users provide training images, and the ACTIT system automatically constructs appropriate image processing procedures. 3D-ACTIT [10, 11] isan extended method that automatically constructs various 3D image processing procedures, and is applied to medical image processing and video image

processing. Recently, two other extensions of ACTIT, Genetic Image Network(GIN) [12] and Feed Forward Genetic Image Network (FFGIN) [13], have been
proposed. Instead of a tree representation, they are represented by a network
structure. Their biggest difference from ACTIT is the structure of connections
between image processing filters. In general, a network structure theoretically includes a tree structure (i.e., a network structure also represents a tree structure).
Therefore, the descriptive ability of a network representation is higher than thatof a tree structure. In GIN and FFGIN, a genotype is a string of integers thatindicate image processing filter types and connections. Other studies show that
GIN and FFGIN automatically construct a simple structure for complex imagetransformation using their network representation [12, 13].

3

Genetic Image Network for Image Classification(GIN-IC)

3.1

Overview

The image classifier GIN-IC consists of image transformation, feature extraction,
and arithmetic operation components. The greatest advantage of GIN-IC is its
image transformation (preprocessing) component, which influences image feature
selection. In other words, GIN-IC generates and selects adequate image featuresby a combination of nodes.
3.2

Structure of GIN-IC

GIN-IC constructs an acyclic network-structured image classifier automatically.
Figure 1 shows an example of the phenotype (feed-forward network structure)and genotype (string representing the phenotype) of the proposed method. Oneof the benefits of this type of representation is that it allows the implicit reuseof 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 original images.
Image transformation nodes execute image transformation using the corresponding well-known image processing filters. We prepare one-input one-output filters
and two-inputs one-output filters in the experiments. Feature extraction nodes
extract an image feature from input images. Arithmetic operation nodes execute arithmetic operations. Image classification is performed using the values of
output nodes. The image classification procedure in GIN-IC is as follows:
1. Image transformation (preprocessing) of original images
2. Feature extraction from images
3. Arithmetic operation and image classification
In GIN-IC, these processes evolve simultaneously.
In GIN-IC, the feed-forward network structure of nodes is evolved, as shownin Figure 1. Numbers are allocated to each node beforehand. Increasingly large

Fig. 1. Example of a structure in GIN-IC (phenotype) and the genotype, which denotes
a list of node types and connections.

numbers are allocated, in order, to input nodes, image transformation nodes,
feature extraction nodes, arithmetic operation nodes, and output nodes. Connections that cannot be executed are restricted at the genotype level; for instance,
the feedback structure is restricted. The nodes take their input from the output
of previous nodes in a feed-forward manner. Furthermore, the image transformation and feature extraction nodes are connected either with input nodes orimage transformation nodes. The arithmetic operation nodes are connected either with the feature extraction nodes or other arithmetic operation nodes; thatis, the connections of nodes obey these data type restrictions, which are basedon the idea of strongly typed genetic programming [15].
To adopt an evolutionary method, GIN-IC uses genotype-phenotype mapping. This genotype-phenotype mapping method is similar to Cartesian Genetic
Programming (CGP). The feed-forward network structure is encoded in the formof a linear string. The genotype in GIN-IC is a fixed length representation andconsists of a string that encodes the node function ID and connections of eachnode in the network. However, the number of nodes in the phenotype can vary
in a restricted manner, as not all of the nodes encoded in the genotype have to
be connected. This allows the existence of inactive nodes. In Figure 1, node No.
8 and 11 are inactive nodes.
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 multi-class image classification procedure using a single
network structure.

3.3

Genetic Operator and Generation Alternation Model

To obtain the optimum structure, an evolutionary method is adopted. The genotype of the proposed method is a linear string. Therefore, it is able to use a standard genetic operator. In this paper, we use mutation as the genetic operator.
The mutation operator affects one individual, as follows:– Select several genes randomly according to the mutation rate P m for eachgene.
– Change the selected genes randomly under the constraints of the structure.
We use (1+4) Evolution Strategy ((1+4) ES) as the generation alternation
model. The (1+4) ES procedure in the experiments works as follows:
1. Set generation counter t = 0. Generate an individual randomly as a parent
M.
2. Generate a set of four offsprings, C, by applying the mutation operation toM.
3. Select the elite individual from the set M + C (the offspring is selected if thesame best fitness with the parent). Then replace M with the elite individuals.
4. Stop if a certain specified condition is satisfied; otherwise, set t = t + 1 andgo to step 2.
Since GIN-IC has inactive nodes, a neutral effect on fitness is caused by genetic
operation (called neutrality [16, 14]). In step 3, the offspring is selected if thesame best fitness with the parent, then the searching point moves even if thefitness is not improved. Therefore, we believe that efficient search is achieved
though simple generation alternation model. This (1+4) ES is also adopted andshowed the effectiveness in CGP [14] which has feed-forward network structure
as well as GIN-IC.

4

Experiments and Results

In this section, we apply GIN-IC to the problem of multi-class texture classification and confirm that using image transformation nodes is effective.
4.1

Settings of the Experiment

In this experiment, we apply GIN-IC to a simple test problem of multi-classclassification of texture images. We use texture images from the publicly available
database VisTex1 . 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 foreach class. All images used in the experiment are grayscale images with a sizeof 64×64 pixels. The number of training images is 60 (10 images for each class).
The training images used in the experiment are displayed in Figure 2.
1

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

Fig. 2. Training images used in the experiment. There are six classes: Bark, Food,
Grass, Metal, Stone, and Fabric.

Table 1. Parameters used in the experiment.
Parameter
Value
Generation alternation model(1+4)ES
Number of generations
112500
Mutation rate (Pm )0.02
Number of image transformation nodes 100 or 0
Number of feature extraction nodes
100
Number of arithmetic operation nodes
100
Number of output nodes
6

To take advantage of the multi-class image classification capabilities of GINIC, we prepared six output nodes, one for each class. The class represented by
the output node with the largest value is considered to be the class of the input
image.
We use the number of correct classifications of training images and the number of active nodes as a fitness function. The fitness function used in theseexperiments is described as follows:f = Nc +

1
Na

(1)

where Nc is the number of correct classifications of training images, and Na is thenumber of active nodes. A higher numerical value indicates better performance. If
the classification performance is the same, the number of active nodes should beTable 2. Discrimination rate for the test images using obtained classifiers (averageover 10 different runs).

Bark
Food
Grass
Metal
Stone
FabricAverageUse image filters Do not use image filters
70.7 %
53.4%
86.1 %
44.1%
88.1 %
84.8%
76.7 %
92.5%
80.4 %
83.4%
94.7 %
92.0%
82.8 %
75.0%

small in this fitness function. We think Nc is more important than Na . However,a weight coefficient may have to be given to Nc and Na .
The parameters used by the proposed method are shown in Table 1. We
assume that the number of image transformation nodes is either 0 (to verify theeffect of using image transformation nodes) or 100. If the number of the imagetransformation nodes is 0, this method resembles classification techniques using
the usual GPs. Results are given for ten different runs with the same parameterset.
We prepare simple and well-known image processing filters as the imagetransformation nodes in the experiment (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, and so on. Seventeen 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, and so on. The arithmetic operation nodes are 20
well-known arithmetic operations, e.g., addition, subtraction, multiplication, division, threshold function, piecewise linear function, sigmoid function, absolutevalue, equalities, inequalities, constant value, and so on. GIN-IC is expected toconstruct a complex image classifier using a combination of these nodes.
4.2

Results and Discussion

When the number of image transformation nodes was 100, GIN-IC achieved
100% classification accuracy for all training images. However, 100% accuracy inclassifying training images was not obtained when the number of image transformation nodes is 0, suggesting the effectiveness of using image transformation
nodes. We believe that the image transformation nodes change the original images to images adequate for classification.
Next, we apply the obtained classifiers to test images. Test images (nontraining images that are similar to training images) are not used in the evolutionary process. The number of test images is 708 (118 images for each class).
The average classification performance over ten different runs for the test images

Fig. 3. Example of obtained structure using GIN-IC.

Bark

Food

Grass

Metal

Stone

FabricFig. 4. Examples of output images at node A in Figure 3.

are shown in Table 2. The values of Table 2 are discrimination rates for eachclass. According to the results, the obtained classifiers affect test images as wellas training images. The result also shows the effectiveness of using image transformation nodes. In particular, the discrimination rates of “Bark” and “Food”
improve when image transformation nodes are used. The discrimination rates of“Metal” and “Stone” are worse on the other hand.
Figure 3 is an example of the obtained structure (feed-forward networkstructured image classifiers) constructed by GIN-IC. GIN-IC constructs the
structure using the image transformation nodes in its network. Examples of output images at node A are shown in Figure 4. From these output images, GIN-IC
transforms the original images to other types of images and also transforms thefeatures of the images. This shows that GIN-IC automatically constructs image

Table 3. Confusion matrix and discrimination rates using the structure of Figure 3
for the test images. Each column of the matrix represents a predicted class, while eachrow represents an actual class. Each class has 118 test images.
Bark Food Grass Metal Stone Fabric Discrimination rate
Bark 94
0
0
13
11
0
79.7%
Food
1 116
0
0
0
1
98.3%
Grass 8
0
97
12
1
0
82.2%
Metal 0
0
0
110
8
0
93.2%
Stone 0
0
0
1
117
0
99.2%
Fabric 0
0
0
0
13
105
90.0%
Average discrimination rate
90.4%

transformation and feature extraction components through learning. Moreover,node A connects to the output node of “Food” and the output image of “Food”
is characteristic of other class images.
Table 3 shows the confusion matrix and discrimination rates using the structure of Figure 3 for the test images. This classifier achieves a discrimination rate
greater than 90%. We can observe that the classifier tends to mistake the “Bark”
class for either the “Metal” or “Stone” class.
In the experiments, we only use simple statistical values as image features. We
consider that the classification performance improves if the appropriate texture
features are prepared.

5

Conclusions and Future WorkIn this paper, we propose a new method for automatic construction of an image classifier that evolves feed-forward network structured image classification
programs. The image classifier of the proposed method consists of image transformation, feature extraction, and arithmetic operation components. The greatest
advantage of the proposed method is the presence of the image transformation(preprocessing) component. We applied the proposed method to the problemof texture classification and confirmed that it obtained the optimum solution.
From the experimental results, the obtained classifier is effective in texture classification. We are, however, recognizing that GIN-IC should be compared withother classifiers in order to evaluate the quality of GIN-IC. In future work, wewill apply the proposed method to other problems of image classification andobject recognition, particularly to larger problems and other types of problems.
Moreover, we will introduce the use of images’ color information for classification.

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