An Improved Fuzzy Algorithm for Image Segmentation

Size: px

Start display at page:

Download "An Improved Fuzzy Algorithm for Image Segmentation"

Aldous Cunningham
5 years ago
Views:

1 World Academy of Science, Engineering Technology An Improved Fuzzy Algorithm for Image Segmentation Majid Gholamiparvar Masooleh, Seyyed Ali Seyyed Moosavi Abstract In this paper we propose a color classification algorithm in which Particle Swarm Optimization method optimizes a fuzzy system for Color Classification Image Segmentation with least number of minimum error rate. In this approach each particle of the swarm codes a set of fuzzy. During evolution, each member of population tries to maximize a fitness norm which has designed due to high classification rate small number of. Finally, the particle with the highest fitness value is selected as the best set of fuzzy for image segmentation. Fuzzy sets are defined on the H, S L components of the HSL Color Space to provide a fuzzy-based model which aims to follow the human intuition of Color Classification. Color-based vision applications face the challenge of color variations by illumination. The Final system designed by this method is adaptive to continuous variable lighting according to its evolvingfuzzy nature. In this method parameters setting's done only once.the experimental results in RoboCup leagues demonstrate that the presented approach can be very robust to noise light variations. Keywords Image Segmentation, Fuzzy reasoning, Particle Swarm Optimization, Fuzzy Color Classification. I. INTRODUCTION ANY color vision systems require a first step of M classifying pixels of a given image into a discrete set of color classes. This early vision step plays an important role in machine vision application. RoboCup soccer is a color-coded environment, where colors are used to define principal objects needed by the robots to perform their tasks. Color variation by illumination is one of the most concerning matters in colorbased vision applications. To address this concern, adaptive color modeling has attracted intense research interests, its use in color tracking applications shows promising results like [1], [2] [3].Good color segmentation allows for easy implementation of object recognition localization. Most of the robot vision systems are based on fast accurate implementation of such processes. Most of the teams in RoboCup soccer, recognize locate objects from a rough segmentation (e.g. [4]), applying more sophisticated recognition techniques (e.g., region growing) at a later stage. However, this second approach may be less reliable or require M. Gholamiparvar. M. was with the Islamic Azad University of Qazvin, Qazvin, Iran. He is now a faculty member of the Computer Engineering Department of Islamic Azad University of Abhar, Abhar, Iran ( gholami.parvar@gmail.com). S. Ali S. Moosavi, is with Islamic Azad University of Qazvin, Qazvin, Iran ( info.moosavi@gmail.com). more computational resources. According to [5], fuzzy approaches for image segmentation can be categorized into four classes: segmentation via threshholding, segmentation via clustering, supervised segmentation, rule based segmentation. Among these categories, rule based approaches are able to take advantage of application dependent heuristic knowledge, model them in the form of fuzzy. In [6], a set of fuzzy are established based on fuzzy variables, which are associated with the membership values of pixels obtained by the fuzzy c-mean clustering approach (FCM) [7] the possibilistic c-mean clustering approach (PCM) [8], to construct a correction matrix for modifying the fuzzy partition matrix. In [9], a fuzzy reasoning method in conjunction with a PSO is employed for color image classification through region merging. In this paper we present a novel approach for color classification which is used in RoboCup soccer in the Middle size League it can be applicable for other machine vision applications as well. The aim of this work is to retrieve images according to their dominant(s) color(s) expressed through linguistic expressions, implementation through a vision system. The main problem occurs by the huge number of fuzzy. These decrease the pace of classifying data. So what we need is an efficient method to classify data while decreasing the number of the concerning. In this paper a Particle Swarm Optimization (PSO) method has been used for an automatic production of fuzzy generation of an optimized membership. The so called PSO method emerges allies itself to Evolutionary Algorithms based on simulation of the behavior of a flock of birds or school of fish, it s proven to have great potential for multi-objective optimization applications [10]. Swarm Algorithms differ from Evolutionary Algorithms most importantly in both metaphorical explanation how they work. What is new with the Swarm Algorithm is that the individuals (particles) persist over time, influencing one another s search of the problem space. This paper organized as follows: The second section contains the fundamental of fuzzy color classification, in the third section optimization using PSO is presented in the fourth section PSO-based Fuzzy Classification System is defined. Our experimental setup results are shown in section five. Finally, conclusions discussions come in section six. 400

2 World Academy of Science, Engineering Technology II. FUZZY COLOR CLASSIFICATION Fuzzy color segmentation is a supervised learning method for segmentation of color images. This method assigns a color class to each pixel of an input image by applying a set of fuzzy on it. A set of training image pixels, for which the colors are known are used to train the fuzzy system at first step. The trained fuzzy system will be later evaluated on test images. Our vision system uses HSL color space for color classification. HSL space (Hue, Saturation Lightness) is a space that characterizes the color directly, thanks to its hue! Indeed hue is enough to recognize the color, except when the color is very pale or very somber. In this space saturation corresponds to the quantity of "white" in the color lightness corresponds to the light intensity of the color. Thus, the identification of color is made in two steps: first H, then L & S, as it can be seen in Fig. 3. As an example, H dimension in Fig. 2 is partitioned into nine trapezoidal membership s each one coding a different color. Another important problem about color spaces is uniformity of scale. HLS space is quite convenient of this problem, but it is a non-ucs (Uniform Color Scale) Space [11]. Indeed our eyes don't perceive small variations of hue when color is green (h = ±85) or blue (h = ±170) while they perceive it very well with orange (h = ±21) for example. Fig. 1 The HSL Space To complete the Modelization, it is necessary to take into account the two other dimensions (L S). Each colorimetric qualifier is associated to one or both dimension(s). To facilitate the process, each dimension interval is divided into three sub-intervals: low value, average value strong value. Thus, we obtain six "one dimension-dependent" qualifiers nine "two dimension-dependent" qualifiers. Fig. 3 shows the nine "two dimension-dependent" qualifiers denoted by Q [12]. Each qualifier of Q is associated to a Function varying between 0 1. Fig. 3 Fundamental Color Qualifiers III. PARTICLE SWARM OPTIMIZATION Particle Swarm Optimization (PSO) is an evolutionary computation technique (a search method based on a natural system) developed by Kennedy Eberhart [14] [15]. Like a Generic Algorithm (GA), PSO is a population based optimization tool. However, unlike GA, PSO has no evolution operators such as Crossover Mutation, moreover, PSO has less parameters. PSO is an evolutionary algorithm that does not implement survival of the fittest, unlike other evolutionary algorithms where an evolutionary operator is manipulated, the velocity is dynamically adjusted. The system initially has a population of rom solutions. Each potential solution, called a particle, is given a rom velocity is flown through the problem space. The Thus, to model the fact that the distribution of colors is not uniform on the circle of hues, Truck in [12], propose to represent them with trapezoidal or triangular Fuzzy Subsets. Several other works have been done in the field of none uniformly distributed scales: for example, Herrera Martinez use Fuzzy Linguistic Hierarchies with more or less labels, depending on the desired granularity [13]. Similarly, [12] associated colors with fuzzy sets. Indeed, for each color of Г they built a Function varying from 0 to 1. If this is equal to 1, the corresponding color is a "true color" (Fig. 2). Fig. 4 Dimensions L S Fig. 2 The Dimension H particles have memory each particle keeps track of its previous best position (called the ) its corresponding fitness. There exist a number of for the respective particles in the swarm the particle with greatest fitness is 401

3 World Academy of Science, Engineering Technology called the global best ( ) of the swarm. The basic concept of the PSO technique lies in accelerating each particle towards its locations, with a rom weighted acceleration at each time step. The main steps in the particle swarm optimization process can be described as follows: (I). Initialize a population of particles with rom positions velocities in d dimensions of the problem space fly them. (II). Evaluate the fitness of each particle in the swarm. (III). for every iteration compare each particle s fitness with its previous best fitness ( ) obtained. If the current value is better than, then set equal to the current value the location equal to the current location in the d-dimensional space. (IV). Compare of particles with each other update the swarm global best location with the greatest fitness ( ). (V). Change the velocity position of the particle. represent the velocity position of the i-th particle with d dimensions, respectively, are two uniform rom s, W is the inertia weight, which is chosen beforeh. ) (1) (VI). Repeat steps (II) to (V) until convergence is reached based on some desired single or multiple criteria. PSO has many parameters these are described as follows: W, called the inertia weight controls the exploration of the search space because it dynamically adjusts velocity. Local minima are avoided by small local neighborhoods, but faster convergence is obtained by a larger global neighborhood, in general a global neighborhood is preferred. Synchronous updates are more costly than the asynchronous updates. is the maximum allowable velocity for the particles (i.e. in the case where the velocity of the particle exceeds, then it is limited to ). Thus, resolution fitness of search depends on. The constants in, termed as cognition social components, respectively, are the acceleration constants which changes the velocity of a particle towards (generally, somewhere between ). The velocities of the particles determine the tension in the swarm. IV. PSO-BASED FUZZY CLASSIFICATION SYSTEM In the PSO-based method, each individual is represented to determine a Fuzzy Classification System. The individual is used to partition the input space so that the rule number the premise part of the generated Fuzzy Classification System are determined. Subsequently, the consequent parameters of the corresponding fuzzy system are obtained by the premise fuzzy sets of the generated Fuzzy Classification System. A set of L individuals called population (P), is expressed as following: (2) In order to evolutionarily determine the parameters of the Fuzzy Classification System, the individual parameter vectors: contains two. The parameter vector consists of the premise parameters of the cidate fuzzy, where B is a user-defined positive integer to decide the maximum number of Fuzzy Rules in the rule base generated by the individual. Here, is the parameter vector to determine the membership s of the j-th fuzzy rule, where is the parameter vector to determine the membership for i-th input variable. The parameter vector is used to select the Fuzzy Rules from the cidate. If, then the j-th cidate rule is added to the rule base. Consequently, the total number of whose value is greater than or equal to 0.5, is the number of Fuzzy Rules in the generated rule base. In order to generate the rule base, the index j of whose value is greater than or equal to 0.5 is defined as where represents the number of the fuzzy in the generated rule base. generates the premise part of the fuzzy rule base generated by the individual. Consequently, the rule base of the generated Fuzzy Classification System is described as follows: r-th rule: if is is is, then belongs to class with where are the fuzzy sets of the generated r-th fuzzy rule. In order to determine the consequent parameters of the r-th fuzzy rule, a procedure is described as follows [16]: Step 1. Step2. Step3. Determine the grade of certainty, r-th fuzzy rule by:, of the 402

4 World Academy of Science, Engineering Technology where (3) Thus, the consequent parameters of the generated Fuzzy Classification System are determined by the above procedure. According to the above description, each individual corresponds to a Fuzzy Classification System. In order to construct a Fuzzy Classification System which has an appropriate number of fuzzy minimize incorrectly classified patterns simultaneously, the fitness has defined as follows: Where are defined as follows: Here patterns, (4) is the number of incorrectly classified is the number of fuzzy in the rule-base of the TABLE I PSO PARAMETERS Parameter Name Symbol Initialization Value Population size L Variable Max number of B 90 Max number of iterations K 400 Constants of fitness 510 Constants of PSO (1,1,1,0.6) generated Fuzzy Classification System, are user-defined constants for the fitness. Subsequently, a PSO-based method is generated to find an appropriate individual so that the corresponding Fuzzy Classification System has the desired performance. Consequently, the overall procedure can be stated as: Step1. Initialize the PSO-based method. (a) Set the number of individuals (L), the maximum number of (B), the number of generations (K), the constants for the fitness ( ) the constants for the PSO algorithm ( ), according to Table I. (b) Generate initial population P, romly. Each individual of the population would be expressed as follows: where, (c) Generate initial velocity vectors velocity is expressed as follows: () romly. Each Step2. Select particle s neighbors. For each particle generate as follows: Select two particles romly, if Fitness[ then, else. Step3. Update the vector follows: If then, () Step4. Calculate the best position met by each individual best position met by its neighbors, by using equations number (4) (5). Step5. Update the velocity vectors positions by using equations number (6) (7). Step6. Decrease the velocities using following equations: Step7. Check stopping criteria: If K then go to Step8; otherwise go to Step3. Step8. Stop: Report particle with the best fitness value as best rule bas. V. SIMULATION RESULTS A. Practical Data Practical Data has been obtained from colored images of Middle Sized RoboCup soccer field. Robustness of the classification in the variable light was aimed. Practical data is classified into 10 different colors (red, orange, yellow, green, cyan, blue, purple, magenta pink) samples were selected from each color class while 200 samples were romly selected as test samples 1000 samples as practical data. Totally, practical data samples 2000 test samples have been used for all of the colors. as Note that fuzzy set. () () has been set with regard to Trapezoidal % TABLE II ANFIS PERFORMANCE Gaussian No. of efficiency % Triangular No. of efficiency % 403

5 World Academy of Science, Engineering Technology First generation Rom Based on ANFIS Trapezoidal % % TABLE III PSO PERFORMANCE Gaussian % % No. of Triangular efficiency % 89 % B. Fuzzy System The fuzzy system has three inputs for each of HSL dimensions. The number of membership s for H, S&L inputs one output are 11, 3, 3 10, respectively. A Sugeno Fuzzy System has been used. C. Initialization One of the most effective factors on the system s efficiency is swarm initialization. This initialization could be rom or based on a specific algorithm like neuro-fuzzy. Therefore, first we generate the with the use of ANFIS software then optimized them. As it can be seen from the results in Table II & III, the efficiency of the algorithm when we use ANFIS algorithm for initialization, is much better than a rom initialization. If we use a rom initialization, the chance of reaching the optimum answer is low because the algorithm might stop at a local maximum, but with a proper initialization it's possible to reach the Overall Maximum. According to results, a Trapezoidal membership has lower efficiency compared to Trapezoidal Gaussian s. In using ANFIS Gaussian membership s has a better efficiency. The number of fuzzy derived by ANFIS is constant, but with using the presented method we could reduce the number of the to one third which makes a big difference. VI. CONCLUSION In this paper, we presented a new method based on Particle Swarm Optimization Fuzzy Logic as an evolutionary approach in robot vision for color image classification. Fuzzy Classification is a Learning Method with observation which we used to classify the different colors. And this Knowledge Based Supervised Fuzzy-Classification uses Particle Swarm Optimization method as an evolutionary algorithm to generate the appropriate number of optimized fuzzy. We also presented the effectiveness of this method in comparison with the conventional method using permissible ranges. Our result show certainly that using this method helps so much in variegated lighting environment also make robots robust to changing the color of their environment in an optimized way. Moreover, color calibration can be undertaken more quickly, as the calibration method encourages the human trainer to identify all possible pixel values for each color of interest, rather than avoiding those that may cause misclassification (e.g. those that occur in shadow or on the borders of different objects within the image). Lastly, object recognition has also improved, due to not only image segmentation performance, but because object recognition routines can reason about the different levels of color uncertainty indicated by core colors, maybe colors, unknown colors. As the results show, this method uses a fewer number of compared to ANFIS method therefore responds in a shorter time, while its efficiency is almost the same as ANFIS. The first generation calculated with ANFIS has a better efficiency compared to a romly initialized generation. REFERENCES [1] Y. Wu T. S. Huang. Color tracking by transductive learning In Proc. IEEE Int l Conf. on Compute. Vis. Patt. Recog., pages , [2] I. Truck, H. Akdag A. Borgi, "Using Fuzzy modifiers in Colorimetery", Proceedings of the 5th World Multi conference on Systemic, Cybernetics Informatics, SCI 2001, pp , Orlo, Florida, USA, [3] K. Nummiaro, E. B. Koller-Meier, L. Van Gool. Object tracking with an adaptive color-based particle filter. Symposium for Pattern Recognition of the DAGM, pages , [4] N. Lovell. Illumination independent object recognition In RoboCup 2005: Robot Soccer World Cup IX, [5] J.-C. Bezdek, J.Keller, K. Raghu, N. H. Pal, Fuzzy models algorithms for pattern recognition image processing, Kluwer Academic Publishers, Boston [6] Y. A. Tolias, S. M. Panas, On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system, IEEE signal Processing Letters, vol. 5,pp , [7] J. C. Bezdek, Pattern recognition with fuzzy objective algorutgns, Plenum Press New York London,pp , 1993 [8] R. Krishnapuram, J. M. Keller, A possibilistic approach to clustering, IEEE pans, fuzzy System,vol,1, pp,98-110,1993 [9] S. Makrogiannis, G. Eeanomou, S. Fatopoulos, "A fuzzy dissimilarity for region based segmentation of color images," int. j. pattern recognition, Artificial Intelligent., vol. 15. pp ,2001 [10] A Coello Coello Carlos M. S Lechuga, MOPSO : A proposal for multiple objective particle swarm optimisation Proceeding of internationl. Conference on Artificial Neural Nets Genetic Algorithmes, ICANNGA 97.Springer Verlag, pp [11] K. Nummiaro, E. B. Koller-Meier, L. Van Gool. Object tracking with an adaptive color-based particle filter. Symposium for Pattern Recognition of the DAGM, pages , [12] I. Truck, H. Akdag A. Borgi, "A Symbolic Approach for Colorimetric Alterations", proceedings of EUSFLAT 2001, , Leicester, Engl, September [13] F. Herrera L. Martinez, "A model based on linguistic two-tuples for dealing with multigranularity hierarchical linguistic contexts in multiexpert decision-making", IEEE transactions on Systems, Man Cybernetics, Part B, 31(2), pp , [14] J. Kennedy R. Eberhart, "Particle swarm optimization," Proceedings, IEEE International Conf. on Neural Networks, Vol. 4, pp [15] J. Kennedy, R Eberhart Y. Shi, Swarm intelligence, Morgan Kaufmann Publishers, [16] Ishibuch, H., Nozaki, K., Yamamoto, N. Tanaka, H., Selecting Fuzzy If-Then Rules for Classification Problems Using Genetic Algortithms, IEEE Trans. Fuzzy Systems, Vol. 3,pp (1995). 404

Argha Roy* Dept. of CSE Netaji Subhash Engg. College West Bengal, India.

Argha Roy* Dept. of CSE Netaji Subhash Engg. College West Bengal, India. Volume 3, Issue 3, March 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Training Artificial