Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
Automatic detection of salient objects and spatial relations in videos for a video database system
Image and Vision Computing
Towards Fully Automatic Image Segmentation Evaluation
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
A comparative evaluation of interactive segmentation algorithms
Pattern Recognition
Generalizing edge detection to contour detection for image segmentation
Computer Vision and Image Understanding
Toward automated evaluation of interactive segmentation
Computer Vision and Image Understanding
A subjective method for image segmentation evaluation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Salient object detection: a benchmark
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Rough sets and neural networks based aerial images segmentation method
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Kidney segmentation using graph cuts and pixel connectivity
Pattern Recognition Letters
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Image segmentation and its performance evaluation are very difficult but important problems in computer vision. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and objectivity: For general-purpose segmentation, the ground truth and segmentation accuracy may not be well defined, while embedding the evaluation in a specific application, the evaluation results may not be extended to other applications. We present in this paper a new benchmark for evaluating image segmentation. Specifically, we formulate image segmentation as identifying the single most perceptually salient structure from an image. We collect a large variety of test images that conforms to this specific formulation, construct unambiguous ground truth for each image, and define a reliable way to measure the segmentation accuracy. We then present two special strategies to further address two important issues: (a) the most salient structures in some real images may not be unique or unambiguously defined, and (b) many available image-segmentation methods are not developed to directly extract a single salient structure. Finally, we apply this benchmark to evaluate and compare the performance of several state-of-the-art image-segmentation methods, including the normalized-cut method, the level-set method, the efficient graph-based method, the mean-shift method, and the ratio-contour method.