RETRACTED ARTICLE: Global biotic cross-pollination algorithm enhanced with evolutionary strategies for color image segmentation

Object segmentation is a prominent and challenging issue pertaining to image analysis and computer vision applications. This segmentation enables a higher number of applications like image retrieval, object recognition and object reconstruction. Considering this importance of object segmentation, the ultimate aim of the proposed research work in this paper is to focus on color image segmentation that is visually triggered using the GBCPA, popularly known as global biotic cross-pollination algorithm dependent on evolutionary strategy models. Evolutionary techniques imitate the procedure of genetic evolution and make utilization of the operators of recombination, mutation and selection to create the new-generation individuals. The new flower pollination optimization algorithm (FPOA) proposed in this paper aims to improve the objective characteristics of the basic FPOA approach. The significant contribution here mainly focused on preventing the premature convergence for dealing with the complicated constraints of the color image segmentation problem. The performance of GBCPA is tested over a Gould segmentation data set comprising of 715 images, both urban and rural images. The data set is tested under various evaluation techniques, and the proposed algorithm results in better accuracy than the other methods considered in comparison with the existing literature.