Performances of Chimpanzee Leader Election Optimization and K-Means in Multilevel Color Image Segmentation

Image segmentation is one of the essential approaches in image processing and analysis required for object detection and background separation. This paper aims to analyze the performance and capabilities of the Chimpanzee Leader Election Optimization (CLEO) algorithm and the K-means clustering technique. The CLEO algorithm is an optimization model used to optimize the segmentation of color objects in images. At the same time, the objective function in finding the best values utilizes the K-means method. Determining the number of clusters in this paper uses the elbow method, where this method functions to obtain the optimal number of clusters in multilevel color image segmentation. This paper displays random images taken from a download library based on flower, animal, and transportation categories. The results of the elbow method on the three selected images in this image segmentation research have taken the number of K values in these categories, namely 4, 5, and 6, respectively. It was to demonstrate the hybrid model capabilities of the CLEO and K-means. The method for evaluating the results of this hybrid model in segmenting images used the Intersection over Union (IoU) metric, Jaccard Index, Pixel Identity, and Localization Error. The IoU metric results for these categories got a score above 99%, the Jaccard index all got a score of 100%, and Pixel Identity got a score below 1.4 %. In contrast, Localization Error scores in the categories used have yielded 15.9%, 29.3%, and 7.7%, respectively.