Learning to detect texture objects by artificial immune approaches

Future Generation Computer Systems 20 (2004) 1197–1208

Learning to detect texture objects by artificial immune approaches Hong Zheng a,∗ , Jingxin Zhang b , Saeid Nahavandi c a

School of Electronic Information, Wuhan University, Wuhan, Hubei 430079, People’s Republic of China Department of Electrical and Computer System Engineering, Monash University, Vic. 3800, Australia c School of Engineering and Technology, Deakin University, Geelong, Vic. 3217, Australia

b

Available online 28 February 2004

Abstract This paper introduces a novel method to detect texture objects from satellite images. First, a hierarchical strategy is developed to extract texture objects according to their roughness. Then, an artificial immune approach is presented to automatically generate segmentation thresholds and texture filters, which are used in the hierarchical strategy. In this approach, texture objects are regarded as antigens, and texture object filters and segmentation thresholds are regarded as antibodies. The clonal selection algorithm inspired by human immune system is employed to evolve antibodies. The population of antibodies is iteratively evaluated according to a statistical performance index corresponding to object detection ability, and evolves into the optimal antibody using the evolution principles of the clonal selection. Experimental results of texture object detection on satellite images are presented to illustrate the merit and feasibility of the proposed method. © 2003 Elsevier B.V. All rights reserved. Keywords: Texture object detection; Artificial immune system; Clonal selection

1. Introduction Satellite images have been being important sources for acquiring geographic information. With the improvement of imagery resolution, the visibility of ground details makes it possible to detect ground objects. Because a lot of important ground objects such as plant coverage and buildings in dense urban area appear textured, texture analysis plays an important role in satellite image interpretation. In other words, the nature of object detection on satellite images is the texture object detection. A wide variety of measures for discriminating textures have been proposed [1,2]. These methods can be divided into two main categories: statistical and structural. The structural approach is suitable for regular structural texture anal∗

Corresponding author. Tel.: +86-27-87653764. E-mail address: [email protected] (H. Zheng).

ysis rather than more natural texture analysis in practice. In the statistical approach, texture is analysed by defined stochastic models such as time series models, fractals, random mosaic models, syntactic models, etc. This approach can be used for regular and irregular texture analysis. In the real world, however, a major problem with the application of texture analysis to real problems is that textures in the real world are often not uniform, due to changes in orientation, scale or other visual appearance. Hence, researchers have been attempting to find robust texture features. In recent years, some researchers presented some approaches based on Gabor filters [3–5], autoregressive random field model [6] and local binary patterns [7] to extract texture features which are invariant to changes in rotation or scale. Unfortunately, the degree of computational complexity of these proposed texture measure is high. In order to solve the problem, You and Zheng [8,9] presented some new methods to extract a robust

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texture feature by guided search procedure and genetic algorithms (GA), respectively. In their methods, the principle texture statistic utilized to represent the texture feature is the normalized “texture energy” derived from Law’s approach, i.e. the mean of pixel gray scale within a 15 × 15 window, which is generated by convolution with the optimal texture filter obtained from task-aimed training. Their experimental results on texture images showed that optimal texture filters could be used to extract robust texture features. However, these approaches only focused on acquiring texture filters automatically, and the threshold for texture segmentation was set manually. In addition, due to GA’s weak ability of local searching and the complexity of texture objects on satellite images, sometimes it is difficult or time consuming to find one optimal texture filter for discrimination of various texture objects. In order to overcome these drawbacks, this paper presents a novel methodology to automatically detect texture objects from panchromatic satellite images. A hierarchical strategy is introduced to detect texture objects from satellite images. The method employs a set of 2D texture filters to extract various texture objects. Each filter is only used to extract two types of texture objects. This method reduces the complexity of texture recognition and makes it easy to acquire optimal texture filters. Moreover, in order to improve the ability of searching the optimal solution, a clonal selection algorithm inspired by biological immune system is used to learn the texture filters and segmentation thresholds, instead of genetic algorithms. The details of the algorithm design are discussed in this paper. This paper is organized as follows. The hierarchical object detection strategy is outlined in Section 2. Section 3 overviews the immune system and its computation model. Section 4 describes the learning algorithm for texture object detection. Section 5 presents the experimental results and conclusions are given in Section 6.

2. Hierarchical object detection strategy Satellite images generally contain texture objects with different roughness. According to the roughness, texture objects can be divided into two categories: rough texture objects and non-rough texture objects.

Rough texture objects represent the objects with strong roughness texture such as building swarms and forest, while non-rough texture objects represent the objects with weak roughness texture such as seas and rivers. Any texture object is either a rough texture object or non-rough texture object. Thus, the procedure to detect texture objects is regarded as the procedure to discriminate the roughness of texture objects. Because different objects have different roughness levels, the detection process can be done hierarchically. Based on the above principle, a hierarchical object detection strategy shown in Fig. 1 is proposed. Firstly, all objects in an original image are segmented into rough texture objects and non-rough texture objects using a texture filter and threshold. Rough texture objects mainly include building swarms and forest, while non-rough texture objects mainly include cultivated lands, rivers, etc. Secondly, rough texture objects and non-rough texture objects are respectively divided into relatively rough texture objects and relatively non-rough texture objects using another filter and threshold. The aim of this step is to extract more detailed objects. For example, we can extract habitations from rough texture objects or rivers from non-rough texture objects. The above process repeats until the interested objects are detected. A hierarchical object detection example is given in Section 5. In the detection procedure, the design of filters and thresholds is a key issue. This paper proposes an artificial immune approach (AIA) to learn these filters and thresholds. The details will be described in Section 4.

3. Artificial immune system The nature immune system is one of the most intricate bodily systems and its complexity can be compared to that of the brain. With the advances in the biology and molecular genetics, the comprehension of how the immune system behaves is increasing very rapidly. The knowledge about the immune system functioning has unravelled several of its main operative mechanisms. These mechanisms have demonstrated to be very interesting not only from a biological standpoint, but also under a computational perspective. Similarly to the way the nervous system inspired the development of artificial neural networks, the immune system has now led to the emergence of

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Fig. 1. The hierarchical texture object detection strategy.

artificial immune systems (AIS) as a novel computational intelligence paradigm [10]. The human immune system protects the body from a large variety of bacteria, viruses, and other pathogenic organisms. It has a multi-layered defense architecture shown in Fig. 2 [11]. The first level is the skin, which is the front barrier to infection. Another barrier is physiological, where conditions such as pH and temperature provide inappropriate living conditions

Fig. 2. The human immune system architecture.

for foreign organisms. Once pathogens have entered the body, they cause the innate immune response and the acquired immune response. The innate immune response is primarily achieved by the endocytic and phagocytic systems. It ingests extracellular molecules and materials and clears the system of both debris and pathogens. The acquired immune response is the most sophisticated and involves a host of cells, chemicals and molecules. It is called acquired because it is responsible for the immunity that is adaptively acquired during the lifetime of the organism. This response mainly recognize foreign cells and molecules by producing antibody molecules that physically bind with antigens. In order to bind the antigen and antibody molecules, their three-dimensional shapes must match in a lock-and-key manner [12]. For every antigen, acquired immune response must be able to produce a corresponding antibody molecule, so that the antigen can be recognized and defended against. The antibody, therefore, can have a geometry that is specific to a particular antigen (specialist) or is capable of partial matching and capturing of a broad group of antigens (generalist). The primary role of this defense mechanism is to distinguish between the self (body cells and tissues) and the non-self (antigens). This discrimination is achieved by two major groups of immune cells, known as B-cells and T-cells [12]. These two types of cells are rather similar, but differ with relation to how they recognize antigens and by their functional roles.

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B-cells are capable of recognizing antigens free in solution (e.g. in the blood stream), while T-cells require antigens to be presented by other accessory cells. Nature immunological mechanisms have inspired the development of several computational models [13]. Among these models, negative selection algorithm, clone selection algorithm and immune network model are often used by researchers. Negative selection algorithm is based on self–non-self discrimination in the immune system. This discrimination is achieved in part by T-cells, which have receptors on their surface that can detect foreign proteins (antigens). The algorithm has been proposed in the literature with applications focused on the problem of anomaly detection, such as computer and network intrusion detection [14], image segmentation [15], and hardware fault tolerance [16]. Clone selection algorithm is also inspired from self–non-self discrimination in the immune system. But it is achieved by B-cells, which produce memory antibodies with high affinity. The whole selection procedure includes proliferation and mutation, which are known as the maturation of the immune response and is analogous to the natural selection of species. In this paper, the system of texture object detection is formulated as an artificial immune system. Texture objects are regarded as antigens, and texture object filters and segmentation thresholds are regarded as antibodies. The clonal selection principle is employed to evolve antibodies. The algorithm details will be described in the next section.

4. Algorithms 4.1. Clonal selection The clonal selection is the theory used to explain how an immune response is mounted when a non-self antigenic pattern is recognized by a B-cell [17]. Fig. 3 illustrates the clonal selection, proliferation and affinity maturation processes [11]. In brief, when a B-cell receptor recognizes a non-self antigen with a certain affinity, it is selected to proliferate and produce antibodies in high volumes. The antibodies are soluble forms of the B-cell receptors that are released from the B-cell surface to cope with the invading non-self antigen. Antibodies bind to antigens leading to their eventual limitation by other immune cells. Proliferation in the case of immune cells is an asexual and mitotic process; the cells divide themselves (there is no crossover). During reproduction, the B-cell progenies (clones) undergo a hyper mutation process that, together with a strong selective pressure, result in B-cells with antigenic receptors presenting higher affinities with the selective antigen. This whole process of mutation and selection is known as the maturation of the immune response [18] and is analogous to the natural selection of species [19]. In addition to differentiating into antibody producing cells, the activated B-cells with high antigenic affinities are selected to become memory cells with long life spans. These memory cells are pre-eminent in

Fig. 3. The clonal selection procedure.

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future responses to this same antigenic pattern, or a similar one [11]. Some authors proposed a clonal selection algorithm to fulfil above clonal selection processes [20]. This algorithm was initially proposed to perform pattern recognition and solve multi-modal optimization tasks. Define a set of patterns to be recognized and call it the non-self set (P). Based upon the clonal selection algorithm, generate a set of detectors (M) that will be responsible to identify all elements that belong to the non-self set. The algorithm as summarized by Castro is as follows [11]: 1. Randomly initialize a population of individuals (M). 2. For each pattern of P, present it to the population M and determine its affinity (match) with each element of the population M. 3. Select n1 of the highest affinity elements of M and generate copies of these individuals proportionally to their affinity with the antigen. The higher the affinity, the greater the number of copies, and vice versa. 4. Mutate all these copies with a rate proportional to their affinity with the input pattern: the higher the affinity, the less the mutation rate, and vice versa. 5. Add these mutated individuals to the population M and reselect n2 of these maturated (optimized) individuals to be kept as memories of the system. 6. Repeat Steps 2–5 until a certain criterion is met, such as a minimum pattern recognition or classification error. Obviously, this algorithm allows the artificial immune system to become increasingly better at its task of recognizing patterns (antigens). Thus, based on an evolutionary-like behaviour, the clonal selection algorithm learns to recognize patterns. 4.2. Learning texture objects detection filters and thresholds using clonal selection principle 4.2.1. Definition of immunological terms In this paper, the immunological terms are defined in the following manner: • Antigen: Any of training texture object images. • Antibody: A float string encoded by filter parameters and a segmentation threshold. Fig. 4 illustrates

W1 W6 W11 W16 W21

W2 W7 W12 W17 W22

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W3 W8 W13 W18 W23

W4 W9 W14 W19 W24

W5 W10 W15 W20 W25

(a) W1

W2

W3

...

Filter parameters

W25

T

Threshold

(b) Fig. 4. Antibody encoding scheme: (a) a 5 × 5 filter architecture, Wi ∈ [−2, 2], i = 1, 2, . . . , 25; (b) an antibody architecture, T ∈ [0, 512].

an antibody structure. Because the layout of parameters of the filter plays the most important role in the texture feature extraction rather than their values [21], we can randomly set the range of the parameters. Here the range of parameters is set from −2 to 2, i.e. Wi ∈ [−2, 2], i = 1, 2, . . . , 25. The maximum value of the threshold is given by Tmax = Wmax × 256, i.e. T ∈ [0, 512]. • Affinity: The percentage of correct detection of an antibody. It is defined by f(%) =

Number of correctly recognized images Total number of training texture images ×100. (1)

The greater the value of f, the higher the antibody’s affinity. For each antibody, the procedure to recognize texture objects consists of the following three steps. (1) Decode an antibody and get a filter and a threshold. (2) Convolve all the training images by the filter. The 2D convolution of the image I(i, j) and the filter A(i, j) with size (2a + 1) × (2a + 1) is given by the relation F(i, j) = A(i, j) × I(i, j) a a

= A(k, l)I(i + k, j + l).

(2)

k=−al=−a

For a 5 × 5 filter, a = 2. (3) Calculate the mean of the convolved training images. The images with mean values greater than the

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threshold are recognized as rough textures, while the images with mean values less than the threshold are recognized as non-rough textures. 4.2.2. Immune evolutionary operation Clone: This operation is to generate copies of every individual in an antibody population proportionally to its affinity with the antigen. All individuals are sorted in descending order firstly. The amount of clones of a antibody is given by   fi ni = round N × N , i = 1, 2, . . . , N, (3) i=1 fi where N is the number of all individuals in an antibody population. It is set at 50 according to Grefenstette’s suggestion [22]. fi is the affinity value of the ith antibody. Obviously, the higher the affinity, the greater the number of copies, and vice versa.

Mutation: The mutation operation creates a new antibody by randomly changing one or more of the unit values in the antibody with a probability proportional to their affinity. The mutation probability is given by Pi =

fmax − fi , fmax − fmin

On-line detection phase

Create an initial antibody population

Input a detected image

Y

Detection

Optimal antibody

N Calculate antibodiesí affinities

(4)

where fmax is the maximum affinity value, and fmin the minimum affinity value. From Eqs. (3) and (4), it can be seen that antibodies with high affinities in a population always have more clone copies and less mutation probabilities than that with low affinities. This corresponds to nature evolutionary mechanism. Reselection: This operation sorts all individuals in descending order, and replaces the d lowest affinity antibodies with d new randomly generated antibodies.

Off-line learning phase

Generations>n?

i = 1, 2, . . . , N,

Rough texture objects

Clone operation

Mutation operation

Reselection operation

Fig. 5. The flowchart of object detection based on the clonal selection principle.

Non-rough texture objects

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4.2.3. Learning algorithms description The whole learning procedure is described as follows: (1) Randomly generate an antibody population (M) which represent a set of filters and segmentation thresholds. (2) Evaluate the affinity of each antibody in the population with Eq. (1). (3) Generate clone copies of all individuals with Eq. (3). (4) Mutate all these copies with a probability given by Eq. (4). (5) All individuals are sorted in descending order, and replace the d lowest affinity antibodies in M, with d new randomly generated antibodies. (6) Repeat Steps 2–5 until a given termination condition is met. In general, a fixed maximum number of generations is allowed as the termination condition. Here it is set at 100 according to our experimental experience. 4.3. Object detection procedure based on the optimized antibodies After learning procedure, the acquired antibody (the texture filter and threshold) are used to recognize antigens (texture objects). Firstly, according to Eq. (2), convolve the test image by the learned filter. Secondly, calculate the mean

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within a (2n + 1) × (2n + 1) window at each pixel point (i, j). The mean is defined as the texture feature TE at the point. It is given by TE(i, j) =

j+n i+n

1 |F(k, l)|, (2n + 1)2

(5)

k=i−nl=j−n

where n = 9. Thirdly, compare the TE value at every point with the learned threshold. If the TE is greater than the threshold, the point belongs to a rough texture object. Otherwise, it belongs to a non-rough texture object. Finally, a post-processing based on morphology dilation and erosion processing is employed to remove noise [23]. Fig. 5 shows the flowchart of the proposed object detection based on the clonal selection principle.

5. Experimental results This section presents experimental results on satellite images. The training texture object images used in the experiment are from 1 meter and 4 meter resolution IKONOS satellite panchromatic images shown in Fig. 6. The number of total training images is 60, and some of them are shown in Fig. 7. These texture objects are divided into two texture classes and each class also contains two sub-classes. One of these two classes belongs to rough texture objects, the other belongs to non-rough texture objects. Fig. 7(a) shows some

Fig. 6. Two part IKONOS satellite images (credit: “spaceimaging.com”). Satellite image resolution: (a) 1 m; (b) 4 m.

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Fig. 7. Samples of training images used by AIA: (a) rough texture object samples; (b) non-rough texture object samples.

rough texture object samples which include different orientation and size building swarm, trees and barren lands. Among these samples, samples in the first two rows are regarded as relatively rough texture objects and samples in the third row are regarded as relatively non-rough texture objects. Fig. 7(b) shows some non-rough texture object samples which include wet lands, river and cultivated lands. Samples in the first two rows and the third row are regarded as relatively rough and non-rough texture objects, respectively. The experiment of texture object detection is performed in two stages. The first stage is the train-

ing phase where the adaptive filters and segmentation thresholds are acquired through learning the training texture object samples using AIA. In our experiments, AIA is contrasted with standard genetic algorithms. Fig. 8 shows the comparison results of acquiring an adaptive filter and a threshold through learning samples in Fig. 7 by standard GA and AIA. The thick graph represents the maximum affinity value per generation, and the thin graph represents the average affinity value of the population per generation. As can be seen from Fig. 8, the maximum affinity value of individuals in GA and AIA is 0.933, i.e. the maximum recognition

Fig. 8. Comparison of evolving procedure of GA and AIA: (a) GA evolutionary behaviour; (b) AIA evolutionary behaviour.

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0.79 0.79 1.04 -0.67 0.53

-0.03 -1.17 -1.49 -1.87 -1.43

0.16 -1.75 -1.56 -1.11 -0.67

-0.35 1.30 1.93 1.68 1.30

1.30 -1.17 1.80 1.23 -0.86

1.75 -0.54 -1.24 1.56 -0.73

1.30 -0.16 0.16 -0.35 1.93

-0.54 -0.10 -0.67 0.16 -0.35

0.54 -1.36 0.67 -1.56 1.30

-1.30 -1.75 1.56 -1.43 -1.55

0.67 0.79 -0.22 0.29 -0.92

0.98 0.22 1.49 -1.17 1.05

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0.67 0.54 0.41 0.41 -1.43

0.54 -0.03 1.05 -0.29 -1.37

Filter A

Filter B

Filter C

TA=100.6

TB=270.3

TC=15.00

(a)

(b)

(c)

-0.41 0.22 -1.24 -2.0 -0.35

Fig. 9. Three antibodies evolved by artificial immune approach: (a) antibody A; (b) antibody B; (c) antibody C.

rate for training images can reach 93.3%. In addition, the average affinity graph in GA is smoother than that in AIA. This is because GA tends to polarize the whole population of antibodies towards the best candidate solution, while AIA focuses on diversity so as to find the optimal solution as soon as possible. From Fig. 8, we can see that AIA can find the optimal individual only after seven generations, while GA cannot find the optimal individual until the 33rd generation. We did 40 learning by AIA and GA, respectively. The average generations of finding the optimal solution was 9 and 30, respectively. This illustrates that AIA is easier to find the optimal solution than GA. This is because the selection and reproduction mechanism adopted by AIA maintains individuals’ diversity so that AIA is easy to jump out of local optima to explore better solutions.

(a)

Fig. 9 shows a set of optimal filters and thresholds acquired by AIA. Filter A is acquired by learning rough texture object samples represented by Fig. 7(a) and non-rough texture object samples represented by Fig. 7(b). Filter B and filter C are generated by learning relatively rough texture object samples represented by the first two rows in Fig. 7(a) and (b) and relatively non-rough texture object samples represented by the third row in Fig. 7(a) and (b), respectively. The second stage is to detect hierarchically interested objects (river and habitation) using optimal filters and thresholds. Fig. 10 shows the results of extracting rough texture objects from images in Fig. 6(a) and (b), respectively. In Fig. 10, two original images are segmented into two parts by filter A and threshold TA . The white part represents the rough texture objects which mainly include buildings, roads, trees

(b)

Fig. 10. (a) Rough texture object detection result (white area) from the image in Fig. 6(a) using antibody A. (b) Rough texture object detection result (white area) from the image in Fig. 6(b) using antibody A.

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Fig. 11. (a) Overlay image of original image in Fig. 6(a) and river detection result (white area) from the black area in Fig. 10(a) using antibody C. (b) Overlay image of original image in Fig. 6(b) and habitation detection result (white area) from the white area in Fig. 10(b) using antibody B.

and some barren lands in habitation areas, and the black part represents the non-rough texture objects which mainly include beaches, river and cultivated lands. From Fig. 10(a), we can see that all man-made objects, even the ship on river, have been detected. From Fig. 10(b), it can be seen that the black area represents most cultivated lands, i.e. most cultivated lands have been detected. Fig. 11(a) shows the result of further extracting relatively non-rough texture objects from the black part in Fig. 10(a) using filter C and threshold TC . It can be seen that these extracted objects are river. Fig. 11(b) shows the result of further extracting relatively rough texture objects from the white part in Fig. 10(b) using filter B and threshold TB . We can see that the extracted objects belong to habitation, which include main roads, free lands and building swarms whose orientations and sizes are different. We did experiments on 20 satellite images and compared the detection results with manually detected results, the average correct rate of detecting river and habitation was 90.1 and 85.3%, respectively. The above results show that the filters and thresholds learned by AIA can be used to detect different objects with different sizes and orientations. From the biological standpoint, the obtained antibodies can cause adaptive immune response.

6. Conclusions This paper has described a novel approach that has been developed for detecting texture objects from satellite imagery using the learning techniques based on clonal selection principle. In this approach, the system of texture object recognition is formulated as an artificial immune system. The parameters (filters and thresholds) used for object detection are defined as antibodies, which are acquired by the immune learning. Once the system generates antibodies, it can response the antigens, i.e. it is able to recognize texture objects. In some earlier investigations [8,9], You and Zheng designed texture filters for differently scaled and rotated textures using guided search procedure and genetic algorithms, respectively. The approach proposed in this paper differs in following major features: (a) You and Zheng attempted to find one optimal filter to discriminate many different textures. However, due to the complexity of texture objects in real world, sometimes it is difficult or time consuming to find one optimal texture filter for discrimination of various texture objects. Contrastively, it is easier to find the population of filters to discriminate various textures. This idea is inspired

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by the T-cells’ self–non-self discrimination in the immune system, and it is proved by McCoy and Devarajan [15]. Thus, in this paper, a group of optimal filters are acquired to extract interested texture objects hierarchically. (b) The output of You and Zheng’s works is only a 5×5 mask with the sum of its terms equal to zero. In proposed approach, not only the produced mask does not have this constraint, but also the optimal segmentation threshold is acquired. (c) Comparing with the optimization algorithms used by You and Zheng, AIA approach utilizes unique clone selection and somatic mutation processes to speed up the procedure of searching the optimal solution. Proper selection of control parameters for an application of AIA is still an open issue. In this work we have taken a fixed population size and generation number. Further experimentations with the adaptive parameter settings of the AIA are necessary. In addition, the ability of proposed approach is limited by training samples, and combination with other texture models and detection methods is not only helpful, but sometimes is also necessary. It is seen by a survey that the AIS is not used widely to image applications. Only few applications to image processing have been published [15,24,25]. This initial study, while promising, shows its potential application in the field of remote sensing image processing. We plan to further implement more properties of AIS for better results and exploit more applications for image processing.

Acknowledgements The authors would like to thank anonymous reviewers for the helpful comments on the manuscript.

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[21] H. Zheng, On study of application of genetic algorithms to image processing and analysis, Ph.D. Thesis, Department of Electronic Engineering, Wuhan University of Surveying and Mapping, PR China, 2000. [22] J.J. Grefenstette, Optimization of control parameters for genetic algorithms, IEEE Trans. Syst. Man Cybernet. 16 (1) (1986) 122–128. [23] P. Soille, Morphological Image Analysis: Principles and Applications, Springer, New York, 1999. [24] S. Srividhya, S. Ferat, An AIS approach to a color image classification problem in a real time industrial application, in: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Arizona, USA, 2001, pp. 2285–2290. [25] D.F. McCoy, V. Devarajan, Artificial immune system for multispectral feature extraction, in: Proceedings of the SPIE Conference on Algorithms for Multispectral and Hyperspectral Imagery IV, Orlando, FL, vol. 3372, 1998, pp. 241–248.

Hong Zheng received his MS degree in signal and information processing and his PhD in photogrammetry and remote sensing from Wuhan Technical University of Surveying and Mapping (China) in 1995 and 2000, respectively. Since 1990, he has been engaging in intelligent image processing and analysis, and published over 30 papers in image processing and computational intelligence. Currently he is working with Wuhan University. His research interests include image processing, computational intelligence and remote sensing.

Jingxin Zhang received his BE, ME and PhD in electrical engineering, all from Northeastern University, China. He has held several academic and research positions in China, Italy and Australia. He is currently a Senior Lecturer at the Department of Electrical and Computer System Engineering, Monash University, Australia. He has published over 70 papers in control and signal processing, and has been involved in the international program committees of several international conferences. He is recipient of Fok Ying Tong Educational Foundation (Hong Kong) for the outstanding young faculty members in China and China National Education Committee Award for the Advancement of Science and Technology. His current research interests are in control and signal processing theory and applications.

Saeid Nahavandi is a Professor in Deakin University currently. Dr. Nahavandi has the membership of several professional societies and institutions including FIEA, CPE, FIEE, CE, MIEEE, MIPENZ, MRNZS. He has been involved in organising several international conferences and is currently on the international technical committee of 12 conferences. He is also the editor of the International Journal of Intelligent Automation and Soft Computing (South Pacific region) and has published over 100 research papers and research articles.