Image Segmentation Using Biogeography Based Optimization (BBO)

Abstract

Image segmentation is an important problem in computer vision to completely understand the image for better results, i.e., identification of homogeneous regions in the image and has been the subject of considerable research for over the last three decades. Many algorithms have been elaborated for this purpose. This paper elaborates two algorithms one is global optimization method Biogeography Based optimization for automatically grouping the pixels of an color image into disjoint homogeneous regions and the other is clustering method Fuzzy K-means algorithm for reducing the computational complexity of image. And then comparison between both the techniques is calculated. In this purposed work these two algorithms are applied to image and performance is evaluated on the basis of computational time. Fuzzy K-means produces results which require more computational time than Biogeography based optimization. Therefore, comparison shows that Biogeography Based Optimization is more reliable and faster approach for image segmentation than Fuzzy K-means clustering algorithm.