GENETIC IMAGE NETWORK (GIN):
AUTOMATICALLY CONSTRUCTION OF IMAGE PROCESSING ALGORITHM
Shinichi Shirakawa and Tomoharu Nagao
Department of Information Media and Environment
Graduate School of Environment and Information Sciences
Yokohama National University
79-7, Tokiwadai, Hodogaya-ku, Yokohama, Kanagawa, 240-8501, Japan
E-mail: shirakawa@nlab.sogo1.ynu.ac.jp
ABSTRACTA new method for automatic construction of image transformation, Genetic Image Network (GIN), is proposed inthis paper. We previously proposed the system of ACTIT(Automatic Construction of Tree-structural Image Transformation). ACTIT constructs tree structured image processing filters using Genetic Programming (GP). Generally, network structure theoretically includes tree structure (i.e. network structure also represent tree structure.). Thus, the description ability of network representation is higher thanthat of tree structure. In this way, we construct complex
image transformations which cannot be constructed by treestructure. We applied GIN to automatically constructing
image transformation and compare GIN with ACTIT andshow effectiveness of GIN.

In order to verify the effectiveness of GIN, we applied
GIN to automatically construction of image transformation.
From several experimental results, it is verified that GIN automatically constructs image transformation. Additionally,obtained structure by GIN is compact and unique, and uses
loop and feedback structure that cannot be constructed byACTIT.
This paper consists of five sections. Section 2 is an
overview of several related works. Section 3 describes Genetic Image Network (GIN). Several experiments are shownin Section 4. Section 5 is devoted to the conclusions and thediscussion of future works.

2. RELATED WORKS
1. INTRODUCTION
This paper introduces a new method for automatic construction of image transformation. This new method, named Genetic Image Network (GIN), uses network representation.
In image processing, it is difficult to select image processing filters to satisfy the transformation from original images to its target images. We previously proposed the system of ACTIT (Automatic Construction of Tree-structuralImage Transformation) [1–4]. ACTIT approximates adequate image transformation from original image to their target image by a combination of several known image filters.
ACTIT constructs tree structural image processing filters
using Genetic Programming (GP) [5, 6]. It is difficult torepresent repetition, loop and recursive structure using treestructure.
Whereas, GIN evolves the network structure of image
processing filters based on instance based learning in similar ways of the case of ACTIT. The biggest difference between GIN and ACTIT is the structure of connections ofimage filters. Generally, network structure theoretically includes tree structure (i.e. network structure also represent
tree structure.). Thus, the description ability of network representation is higher than that of tree structure. We expect
GIN to automatically construct a simple structure for imagetransformation using its network representation.

2.1. Genetic Programming and Graph based GeneticProgramming
Genetic Programming (GP) [5, 6] is one of Evolutionary
Computation techniques, which was introduced by Koza.
GP evolves computer programs, which are usually tree structure, and searches a desired program using Genetic Algorithm (GA). Today, a lot of extensions and improvements ofGP are proposed.
Parallel Algorithm Discovery and Orchestration (PADO)[7, 8]is one of the graph based GPs instead of the tree structure. PADO uses stack memory and index memory. Theexecution of PADO is carried out from the start node to theend node in the network. PADO was applied to the object
recognition problems.
Another graph based GP is the Parallel Distributed Genetic Programming (PDGP) [9]. In this approach the tree isrepresented as a graph with functions and terminals nodes
located over a grid. In this way it is possible straightforward
to execute several nodes concurrently.
Recently, Genetic Network Programming (GNP) [10–12] which has a directed graph structure is proposed. GNPis applied to make the behavior sequences of agents andshows better performances compared to GP.

Phenotype

2.2. Automatic Construction of Tree structural Image
Transformation (ACTIT)
ACTIT [1–4] constructs tree structured image processing
filters with one-input one-output filters and two-inputs oneoutput filters by using Genetic Programming (GP) to satisfy the given several image examples. The leaf nodes ofa tree are the original images and other nodes are the image processing filters. We give “Training Image Set”, andACTIT system constructs appropriate image processing automatically. We have solved many kinds of image processing problem by using ACTIT. 3D-ACTIT [3, 4] is extended
method which automatically constructs various 3D image
processing procedures, and applies to medical image processing.
3. GENETIC IMAGE NETWORK (GIN)

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3.1. Structure of GIN
Fig. 1. An example of Genetic Image Network.
Genetic Image Network (GIN) is a novel method for automatically construction of image transformation. The representation of GIN is network structure, and GIN is composedof several nodes which are well-known image filters whose
inputting is one or two.
Figure 1 illustrates an example of Phenotype (networkstructure) and Genotype (strings representing Phenotype) in
GIN. Genotype in GIN consists of strings which indicate
image filter type and connections.
We expect GIN to automatically construct a simple structure for image transformation using its network representation.
Initially, we set original images to “in” nodes when GIN
executes. All nodes synchronously transform each inputtedimages, and output the transformed image to destination
nodes. After predefined iterations (we call this parameter“the number of steps”), the images of output nodes are evaluated.
The nodes which have no inputted images do not operation. The two-input node which has only one inputtedimage outputs that same one.
3.2. Genetic Operators
To obtain the best structure of GIN, an evolutionary method
is adopted. As usual, GIN uses crossover and mutation as
the genetic operators.
The crossover operator affects two individuals, and theuniform crossover is used, as follows;
1. Select several nodes randomly according to the crossover rate Pc for each node.
2. The selected nodes with the same node number areswapped between two parents.
Figure 2 shows an example of crossover. In Figure 2,node 1, node 3 and node 4 are selected. The selected nodes
are swapped, and generate offspring.
The mutation operator affects one individual.

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Fig. 2. Crossover operation of two individuals.

1. Select several conections and type of nodes randomlyaccording to the mutation rate Pm for each conection
and type.
2. The selected connections and type of nodes are randomly canged.
Figure 3 shows an example of mutation. The selected strings
are randomly changed.
4. EXPERIMENTS
In this section several experiments with GIN and ACTIT areperformed.
4.1. Setting of Experiments“Training Image Set” we used in these experiments appears
in Figure 4. The goal of these experiments is to obtain imagetransformation of extraction processing of “key” and “coin”.

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A (2 , 3) B* (4 , 2) F* (5 , 5) G (2, in) E (5 , in)Fig. 3. Mutation operation of one individual.

(a) Original image 1.

(b) Target image 1.

(c) Original image 2.

(d) Target image 2.

Fig. 4. “Training Image Set” used in experiments.

We use the mean error on the “Training Image Set” as afitness function. The fitness function used in these experiments is :W PHPn n|−t|o
Nij ij
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i=1 j=1
f itness =1−(1)N n=1 W · H · Vmax 





where on is the transformed image and tn is its target one.
The numbers of pixel in the direction i and j are W , H
respectively, and the number of training image sets is N .
The range of this fitness function is [0.0, 1.0]. The higher
the numerical value indicates the better performance.
The common parameters between the two methods (GINand ACTIT) are identical. The parameters of GIN and ACTIT are given in Table 1 and Table 2, respectively.
We prepare “nop” node which means no operation for
GIN. Therefore, it is possible GIN to represent less processing using more the number of steps.
4.2. Experimental Results
Results are given for 20 different runs with the same parameter set. In this experiment, the parameters of the number ofsteps in GIN are 5, 10, 15 and 20. Figure 5 is the transitionof fitness of ACTIT and GIN. According to the result, GIN
obtains a good solution as well as ACTIT.

Table 1. Parameters of GIN algorithm.
Parameter
Value
The number of generations 500
Population size
101
Crossover rate Pc
0.1
Mutation rate Pm
0.02
Selection
Tournament selection
Tournament size
2
The number of nodes
30
The number of steps
5, 10, 15, 20

Table 2. Parameters of ACTIT algorithm.
Parameter
Value
The number of generations
500
Population size
101
Crossover rate
1.0
Mutation rate
0.9
Selection
Tournament selection
Tournament size
2
Maximum number of functions 30

It is poor performance when we use “5” with the numberof steps. Because it is difficult to obtain complex structureusing low the number of steps.
The output images of “Training Image Set” using GIN
and ACTIT are shown in Figure 6 and 7.
The images of both methods are similar to target images. The both methods automatically construct network
structured or tree structured image transformation. We consider that the performance of GIN is identical ACTIT in thisexperiment.
4.3. Experiment to Test Images
In this section we apply the constructed network or tree
structured image filters by GIN and ACTIT to test images
which are not used in evolutionary process. The test image
used in this experiment are shown in Figure 8.
Figure 9 and 10 are the output images using constructed
network or tree structured image filters by GIN and ACTIT, respectively. GIN and ACTIT transform test images
to ideal images which extracted “key” and “coin”. It showsthat GIN and ACTIT automatically construct general imagetransformation through learning.
4.4. Obtained Structure
Figure 11 is obtained structure by GIN. The parameter ofthe number of steps is 15. It includes loop and feedback
structure and reuse processed image. These structure cannot
be construct by ACTIT.
Each node of GIN synchronously transforms each inputted images, and output the transformed image to destination nodes. In Figure 11, the output image of each nodeis changing every steps. Finally, input image is transformed

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Fig. 9. Output images of GIN using test images in Fig.8.
500

Fig. 5. The transition of fitness of ACTIT and GIN using“Training Image Set” in Figure 4. The number of steps inGIN uses 5, 10, 15 and 20. Each curve is an average over20 runs.

(a) Output image 1.

(b) Output image 2.

Fig. 10. Output image of ACTIT using test images in Fig.8.

(a) Output image 1.

(b) Output image 2.

Fig. 6. Output images of “Training Image Set” using
GIN(15).

(a) Output image 1.

(b) Output image 2.

Fig. 7. Output images of “Training Image Set” using ACTIT.

(a) Original image 1.

(b) Original image 2.

Fig. 8. Test images which are not used in evolutionary process.

to ideal output image. Although the obtained network structure by GIN is compact, actual image processing is verycomplex.
4.5. Extension to Plural Outputs using GIN
GIN constructs network structured image filters, thus it ispossible to represent plural outputs. GIN enables to simultaneously construct plural image transformation using onlya single network structure. It is also possible GIN to reuse
the processed image in its network structure. Whereas, ACTIT must be one output, because it uses tree structure forrepresentation of image transformation.
In this section we experiment to construct plural imagetransformation using GIN. We use the same “Original Images” in Figure 4. There are two “Target Images” in each
“Training Image Set” which appear in Figure 12. The goalsof this experiment are to extract only “key” and only “coin”
at the same time simultaneously.
In this experiment, we use the same parameter in Table
1, and the parameter of the number of steps is 15. GIN hasto simultaneously construct different image transformationin its network structured filters.
The results of “Training Image Set” are shown in Figure
13. GIN obtained the two image transformations using its
network representation.
Next, we apply the constructed network structured image filters to “Test Images” in Figure 8. The results are inFigure 14. The processing of extracting only “coin” wassuccess, extracting “key”, however, failed.
If we use more “Training Image Set” or more varietyof image processing filters, GIN enables to represent more
complex image processing. Therefore, it will be possible toconstruct successful and general network structured imagetransformation which has plural outputs using GIN.

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Fig. 11. An example of constructed structure by GIN.

(a) Target image 1 (1).

(b) Target image 1 (2).

(a) Output image 1 (1).

(b) Output image 1 (2).

(c) Target image 2 (1).

(d) Target image 2 (2).

(c) Output image 2 (1).

(d) Output image 2 (2).

Fig. 12. “Target Images” which are extracted only “key”
and only “coin”. (1) is extracted only “key”. (2) is extracted
only “coin”.

Fig. 13. Output images of “Training Image Set” using
GIN(15). (1) is extracted only “key”. (2) is extracted only“coin”.

5. CONCLUSIONSIn all test cases GIN shows good performance as well as
ACTIT. Obtained structure of GIN is unique and compact
which include loop and feedback.
We extend GIN to plural outputs which cannot construct
by ACTIT. GIN construct ideal network structured image

A novel automatic constructing of image transformation which is Genetic Image Network (GIN) has introduced in thispaper. We tested the performance of GIN against ACTIT.

[6] John R. Koza. Genetic Programming II: Automatic
Discovery of Reusable Programs. MIT Press, Cambridge, MA, USA, 1994.

(a) Output image 1 (1).

(b) Output image 1 (2).

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Visual Learning, pages 81–116. Oxford University
Press, 1996.
[8] Astro Teller and Manuela Veloso. Program evolutionfor data mining. The International Journal of ExpertSystems, 8(3):216–236, 1995.

(c) Output image 2 (1).

(d) Output image 2 (2).

Fig. 14. Output images of “Test Image” using GIN(15). (1)is extracted only “key”. (2) is extracted only “coin”.

filters and shows effectiveness.
In order to clarify the effectiveness of GIN, more experiments have to be run on a variety of test problems. In future
works we plan to extend tests to other problems.
The parameters of executing iteration number and thenumber of nodes have to be predefined in this paper. We
also plan to develop automatic determination of these parameters.
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