A New Similarity Measure and Hierarchical Clustering Approach to Color Image Segmentation
Radhwane Gherbaoui, Nacéra Benamrane, Mohammed Ouali · 2023
Cluster analysis is an important task in data analysis and machine learning. Traditional clustering methods, such as partitioning and density-based approaches, have limitations in identifying natural clusters in datasets with elliptical and chained shapes. In this paper, we propose a novel hierarchical clustering algorithm for color image segmentation that addresses these limitations by quantifying the degree of overlap between clusters as a similarity measure for the merging process.