A Genetic Algorithm Approach to Segment Household Objects from an Image

Abstract

In the field of computer image, Segmentation of colour image is being considered as one of the challenging problems. In order to effectively carry out tasks assigned by human operators, robots operating in domestic environments should be able to detect and distinguish the things utilized in the home. Segmentation enables robots to understand the context of the environment by identifying and categorizing objects.  An Image Segmentation using Genetic algorithm is presented in this work. It is a natural evolutionary approach for optimization problems. In most of the colour image segmentation techniques the clustering is used at beginning to segregate colour images and then Genetic Algorithm (GA) is applied just as an optimization tool. Here K-means Clustering technique along with genetic algorithm has been used to find optimal thresholds between the multiple objects and the complex background. For optimization a Genetic algorithm is applied on the clustering output in which segmentation is improved through different steps of GA. The experimental results shown that the combination of K-means and GA gave promising results with an accuracy of 90% on home object dataset.  Also, a comparison is made between the suggested algorithm and the most widely used watershed segmentation algorithm and it is proved that our algorithm has given equivalent results on both standard and real time datasets.