Evaluation of Segmentation Quality
via Adaptive Composition of Reference Segmentations
Bo Peng, Lei Zhang,
Xuanqin Mou and Ming-Hsuan Yang
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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. |
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