International Journal of Computer Trends and Technology (IJCTT) – volume 11 number 2 – May 2014 ISSN: 2231-2803 http://www.ijcttjournal.org Page69 A Meta-heuristic Approach for Image Segmentation using Firefly Algorithm Bhavana Vishwakarma #1 , Amit Yerpude *2 # M.Tech Scholar, Department of Computer Science and Engg., CSVTU University Rungta College of Engg. & Technology, Bhilai(C.G.), INDIA * Associate Professor, Department of Computer Science and Engg., CSVTU University Rungta College of Engg. & Technology, Bhilai(C.G.), INDIA Abstract — Image segmentation is one of the basic and important steps of image processing. Various methods for image segmentation using clustering techniques are available. The paper proposes a new meta-heuristic image segmentation approach which gives better result in comparison of other clustering technique (K-means). The experimental results show the effectiveness of proposed algorithm. Keywords — Image Segmentation; Firefly Algorithm; K- Means Clustering I. INTRODUCTION Image Segmentation is one of the main steps of image processing, in which any image is being subdivided into multiple segments. Each segment will represent some kind of information to user in the form of color, intensity, or texture. Hence, it is important to isolate the boundaries of any image in the form of its segments [1]. Image segmentation is the first step and also one of the most difficult tasks of image analysis, which has objective of extracting information represented in the form of data from image. [2] [3]. In order to facilitate practical manipulation, recognition, and object-based analysis of multimedia resources, partitioning pixels in an image into groups of coherent properties is indispensable. This process is regarded as image segmentation [4] The k-means algorithm is an iterative technique used to partition an image into k clusters. At first, the pixels are clustered based on their color and spatial features, where the clustering process is accomplished. Then the clustered blocks are merged to a specific number of regions. This approach thus provides a feasible new solution for image segmentation which may be helpful in image retrieval [5]. Firefly algorithm is one of swarm based algorithm that use for solving optimization problems and has been found superior over other algorithms in solving optimization problems. This algorithm is based on the flash producing behaviour of fireflies. [6] Firefly algorithm is recently used for data clustering. A hybrid approach for data clustering using firefly algorithm integrated with k-means, called KFA, has been proposed [7] II. FIREFLY ALGORITHM Firefly Algorithm (FA) was introduced by X. S. Yang [8] in 2008 based on flashing behaviour of fireflies. [9] Firefly uses its flash as a communication medium to attract other fireflies [10]. Firefly algorithm was developed to solve the continuous optimization problems initially. Firefly algorithm employs three idealized rules [11]: 1. All fireflies are unisex so that one firefly will be attracted to other fireflies regardless of their sex; 2. Attractiveness is proportional to their brightness, thus for any two flashing fireflies, the less brighter one will move towards the brighter one. The attractiveness is proportional to the brightness and they both decrease as their distance increases. If there is no brighter one than a particular firefly, it will move randomly; 3. The brightness of a firefly is affected or determined by the landscape of the objective function. In the firefly algorithm, there are two important issues: the variation of light intensity and formulation of the attractiveness. For simplicity, we can always assume that the attractiveness of a firefly is determined by its brightness which in turn is associated with the encoded objective function. The light intensity I(r) varies with distance ‘r’ monotonically and exponentially, is given by: I = I 0 e -γr Where I 0 the original light intensity and Ȗ is is the light absorption coefficient. The attractiveness β of a firefly is defined by:       Where ‘r’ is the distance between each two fireflies and ȕ 0 is their attractiveness at r = 0