Abstract

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.

Highlights

  • Various association of image segmentation exists with high level computer vision and pattern recognition tasks like image classification, content based image retrieval, object identification, recognition and scene understanding

  • The tremendous growth of image segmentation methods make the design of taxonomy more challenging as more and more new recent techniques and approaches are emerging for segmentation based computer vision applications

  • The authors found a gap in the present taxonomy found in the literature; which is mostly found to be focused on the segmentation techniques categorization rather than portraying the overall aspects of its purpose and multi-fold segmentation facets involved in the process

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Summary

Introduction

Various association of image segmentation exists with high level computer vision and pattern recognition tasks like image classification, content based image retrieval, object identification, recognition and scene understanding. Any process adopted by a researcher’s work in the field of segmentation is mentioned as technique and can be categorized using the proposed Multi-Faceted Hierarchical Image Segmentation Taxonomy (MFHIST). The tremendous growth of image segmentation methods make the design of taxonomy more challenging as more and more new recent techniques and approaches are emerging for segmentation based computer vision applications.

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