Evaluation of Segmentation Quality via Adaptive Composition of Reference Segmentations

Bo Peng, Lei Zhang, Xuanqin Mou, Ming Hsuan Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

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.

Original languageEnglish
Article number7723880
Pages (from-to)1929-1941
Number of pages13
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume39
Issue number10
DOIs
Publication statusPublished - 1 Oct 2017

Keywords

  • image segmentation dataset
  • Image segmentation evaluation
  • segmentation quality

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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