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Title: Evaluation of segmentation quality via adaptive composition of reference segmentations
Authors: Peng, B
Zhang, L 
Mou, X
Yang, MH
Keywords: Image segmentation dataset
Image segmentation evaluation
Segmentation quality
Issue Date: 2017
Publisher: IEEE Computer Society
Source: IEEE transactions on pattern analysis and machine intelligence, 2017, v. 39, no. 10, 7723880, p. 1929-1941 How to cite?
Journal: IEEE transactions on pattern analysis and machine intelligence 
Abstract: Evaluating image segmentation quality is a critical step for generating desirable segmented output and comparing performance of algorithms, among others. However, automatic evaluation of segmented results is inherently challenging since image segmentation is an ill-posed problem. This paper presents a framework to evaluate segmentation quality using multiple labeled segmentations which are considered as references. For a segmentation to be evaluated, we adaptively compose a reference segmentation using multiple labeled segmentations, which locally matches the input segments while preserving structural consistency. The quality of a given segmentation is then measured by its distance to the composed reference. A new dataset of 200 images, where each one has 6 to 15 labeled segmentations, is developed for performance evaluation of image segmentation. Furthermore, to quantitatively compare the proposed segmentation evaluation algorithm with the state-of-the-art methods, a benchmark segmentation evaluation dataset is proposed. Extensive experiments are carried out to validate the proposed segmentation evaluation framework.
ISSN: 0162-8828
EISSN: 1939-3539
DOI: 10.1109/TPAMI.2016.2622703
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