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An abundance of various segmentation techniques are available in the literature, that cater to wide range of image understanding applications. The paper proposes a unified way of systematic categorization of the research work on image segmentation called Multi-Faceted Hierarchical Image Segmentation Taxonomy (MFHIST), which consist of six facets presented in a hierarchical manner - scope, requirement, control, feature, image representation and approach specifications. Every scope is exemplified with research works from the literature for better understanding. The paper gives an illustration of populating MFHIST, to provide the reader a quick grasp of few important state-of-art image segmentation research works and their adaptations. As a case study, the illustrations display a limited version to uncover the journey of basic to modern adaptations in the areas region based segmentation approach, such as Markov Random Fields, Spectral Clustering, Active Contour Model, Mean Shift Clustering. The other segmentation approaches have not been considered here, owing to the enormous volume of works in the past four to five decades and limitation in articulating all of them using MFHIST. The performance analysis of the algorithms using quantitative metrics is not in the present scope and will be considered in future version of MFHIST.