ImageSegmentationEvaluation

Supervised metrics

• FBoundary
  • Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues. David R. Martin, Member, IEEE, Charless C. Fowlkes, and Jitendra Malik, Member, IEEE
• Precision
• FMeasure
• Segmentation Covering
• Variation of Information
• RandIndex
• FMeasure for regions
• Precision Recall for Objects and Parts
  • Measures and Meta-Measures for the Supervised Evaluation of Image Segmentation Jordi Pont-Tuset and Ferran Marques. Universitat Politecnica de Catalunya BarcelonaTech
• Boundary Displacement Error

References:

• SEISM: Supervised Evaluation of Image Segmentation Methods

Unsupervised metrics

• ECW
  • The use of visible color difference in the quantitative evaluation of color image segmentation. Hsin-Chia Chen and Sheng-Jyh Wang
• Zeboudj
  • Zéboudj, Rachid. Filtrage, seuillage automatique, contraste et contours: du pré-traitement à l'analyse d'image. Diss. Saint-Etienne, 1988.
  • Unsupervised Evaluation of Image Segmentation Application to Multi-spectral Images
• ValuesEntropy
  • An Entropy-based Objective Evaluation Method for Image Segmentation. Hui Zhang*, Jason E. Fritts and Sally A. Goldman
• LiuYangF
  • Multiresolution Color Image Segmentation. Jianqing Liu and Yee-Hong Yang, Senior Member, IEEE
• FPrime
  • Quantitative evaluation of color image segmentation results. M. Borsotti a, P. Campadelli a,2, R. Schettini b.
• Q
  • Quantitative evaluation of color image segmentation results. M. Borsotti a, P. Campadelli a,2, R. Schettini b,
• FRCRGBD
  • Fusion of geometry and color information for scene segmentation. IEEE Journal of Selected Topics in Signal Processing, 6(5), 505-521. Dal Mutto, C., Zanuttigh, P., & Cortelazzo, G. M. (2012).