User-Friendly Interactive Image Segmentation Through Unified Combinatorial User Inputs

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Extreme Clicking for Efficient Object Annotation (2017, methodology): “To reduce annotation time many interactive segmentation techniques have been proposed, which require the user to input either a bounding box around the object [42, 51, 71], or scribbles [3, 16, 24, 26, 27, 42, 50, 64, 65, 72], or clicks [31, 69].”
Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation (2015, methodology): “Examples of interactive image segmentation using bounding box (a ∼ b) [21] or scribbles (c ∼ d) [22].”
Lazy Random Walks for Superpixel Segmentation (2014, methodology): “In order to provide more intelligent ways to understand the intention of user inputs, Yang et al. [22] proposed a constrained RW algorithm to facilitate the multiple user inputs with an interactive editing framework.”
Y Qualification Main task Y Return N N Y User qualified ? N Feedback Pass ? Pass ? Annotator training Annotating images (2017, methodology): “To reduce annotation time many interactive segmentation techniques have been proposed, which require the user to input either a bounding box around the object [42, 51, 71], or scribbles [3, 16, 24, 26, 27, 42, 50, 64, 65, 72], or clicks [31, 69].”
MILCut: A Sweeping Line Multiple Instance Learning Paradigm for Interactive Image Segmentation (2014, methodology): “The GrabCut dataset has been used as a benchmark in multiple previous works [9, 20, 28, 39].”