A New Similarity Measure and Hierarchical Clustering Approach to Color Image Segmentation

Abstract

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.