A survey of recent interactive image segmentation methods
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A survey of recent interactive image segmentation methods Hiba Ramadan1 (
), Chaymae Lachqar2 , and Hamid Tairi1
c The Author(s) 2020.
[3], and medical image segmentation [4, 5], few works have been dedicated to interactive image segmentation (IIS) methods, although research in this area is very active and a recent periodic overview remains necessary. In fact, except for the comparative evaluation in Ref. [6] of some IIS techniques proposed before 2010 and the work of Ref. [7] in 2013, the only work to do so, as far as we are aware, is Ref. [1] which briefly addressed IIS as part of its survey. IIS, “supervised segmentation”, and “semiautomatic segmentation” all mean the task of extracting an image region or object of interest from the background (BG) using prior knowledge provided by user interaction. This interaction, either in the form of some points or scribbles to mark the object of interest and/or the BG, either using a bounding box (BB) or polygon to delimit the region of interest (ROI), allows the user to provide good constraints (on size, color, location, objectness ...) to guide the segmentation process. This can improve results as well as reducing runtime compared to automatic segmentation methods [7]. In fact, many computer vision applications (medical imaging, image editing, object recognition, and object tracking) need such user intervention to obtain accurate segmentation results, which are then used as input for other highlevel processing. IIS methods can be classified in different ways depending on the criteria used. User interaction: the type of user interaction required can be used to divide methods into seedbased and ROI-based approaches [8] or into active and passive interaction-based approaches [9]. Methodology: the methodology used to segment the desired object can be based either on contours or label propagation [1]. In this work, IIS methods are divided into: contour, graph cut (GC), random walk (RW), and region merging (RM)/region growing
Abstract Image segmentation is one of the most basic tasks in computer vision and remains an initial step of many applications. In this paper, we focus on interactive image segmentation (IIS), often referred to as foreground–background separation or object extraction, guided by user interaction. We provide an overview of the IIS literature by covering more than 150 publications, especially recent works that have not been surveyed before. Moreover, we try to give a comprehensive classification of them according to different viewpoints and present a general and concise comparison of the most recent published works. Furthermore, we survey widely used datasets, evaluation metrics, and available resources in the field of IIS. Keywords
1
interactive image segmentation; user interaction; label propagation; deep learning; superpixels
Introduction
The main goal of image segmentation is to divide an image into homogeneous regions according to common characteristics such as spatial position, color, shape, texture, and motion (in the case of video segmentation).
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