Image Segmentation Using Multi level Artificial Bee Colony Algorithm

Abstract

Image segmentation is considered as significant process in image processing. Since output of image segmentation can be applied in various areas. Several well known conventional approaches of image segmentation such as Otsu and Kapur tend to use lot of computation time which grows exponentially as the number of cluster increases. Thus recent study of image segmentation uses swarm based algorithm. The swarm based algorithms stand out for their ability in generating high quality solutions for solving various problems. Among swarm based algorithms which are commonly used in image segmentation, Artificial Bee Colony (ABC) is considered as one of the best algorithm due to its convergence speed and segmentation result. Most of prior studies define the number of image region in initialization phase. However, identifying the correct number of image region is not a trivial problem. Since the high diversity of image region in real image, thus the correct number is not known a priori in real image. This research propos es novel method for image segmentation using multi level ABC. The first level ABC segmentation aims to define temporary set of threshold for image segmentation. Whereas the second level ABC segmentation will refine thresholds of the previous step. Between those phases, the number of threshold which divide image into region will be optimized. The proposed method shows to improve image segmentation quality within faster computation time.