Classification of Objects and Background Using Parallel Genetic Algorithm Based Clustering

Abstract

In this paper, two novel strategies have been proposed to obtain segmentation of an object and background in a given scene. The first one, known as Featureless(FL) approach, deals with the histogram of the original image where Parallel Genetic Algorithm (PGA) based clustering notion is used to determine the optimal threshold from the discrete nature of the histogram distribution. In this regard, we have proposed a new interconnection model for PGA. The second scheme, the Featured Based(FB) approach, is based on the proposed featured histogram distribution. A feature from the given image is extracted and the histogram corresponding to the derived feature pixels is used to determine the optimal threshold for the original image. The proposed PGA based clustering is used to determine the optimal threshold. The performance of both the schemes is compared with that of Otsu's and Kwon's method and FB method is found to be the best among the three techniques.