Image segmentation evaluation: A survey of unsupervised methods

By Hui Zhang, Jason E. Fritts, Sally A. Goldman. Computer Vision and Image Understanding, 2008

1,104 citations recorded by the source corpus.

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Segment Optimization and Data-Driven Thresholding for Knowledge-Based Landslide Detection by Object-Based Image Analysis (2011, neutral): “[27], an objective evaluation”
New Local Thresholding Method for Soil Images by Minimizing Grayscale Intra‐Class Variance (2013, methodology): “Commonly used measures are based on misclassiication error, edge mismatch, relative foreground area error, region nonuniformity measure, or a combination of these (Zhang, 1996; Sezgin and Sankur, 2004; Zhang et al., 2008; Wang et al., 2011).”
A comparative evaluation of interactive segmentation algorithms (2010, neutral): “The need for semantic objects means unsupervised evaluation techniques [4] and measures of empirical goodness are inappropriate: the features that characterize good semantic objects are decidedly more difficult to measure without ground-truth than those that characterize good homogeneous regions.”
Unsupervised image segmentation evaluation and refinement using a multi-scale approach (2011, positive): “The global intraand inter-segment values are then combined to assign an overall ''goodness'' score to the segmentation (Zhang et al., 2008).”
A REVIEW ON IMAGE SEGMENTATION TECHNIQUES (2016, background): “Image segmentation is a classical and fundamental problem in many applications such as medical image processing, bio metrics, object tracking and recognition, video and computer vision applications [8, 4].”