Common approach: Berkeley Segmentation Dataset (BSDS300) [1] using the precision-recall framework introduced
in [2]. Taken from [3].
The BSDS300 consists of 200 training and 100 test images, each with multiple ground-truth segmentations.
An extension of the BSDS300 is created (BSDS500) [3]. It is is an extension of the BSDS300, where the original 300 images are used for training / validation and 200 fresh images, together with human annotations, are added for testing. Each image was segmented by five different subjects on average. Performance is evaluated by measuring Precision / Recall on detected boundaries and three additional region-based metrics.
Link to download BSDS500 : http://www.eecs.berkeley.edu/Research...
See also this publication: 'Benchmarking Image Segmentation Algorithms' [4]
And these links:
http://www.cs.berkeley.edu/~fowlkes/p... http://www.eecs.berkeley.edu/Research... http://www.eecs.berkeley.edu/Research...
[1] D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human
segmented natural images and its application to evaluating segmentation
algorithms and measuring ecological statistics,” ICCV,
2001.
(http://digitalassets.lib.berkeley.edu...)
[2] D. Martin, C. Fowlkes, and J. Malik, “Learning to detect natural
image boundaries using local brightness, color and texture cues,”
PAMI, 2004.
(http://www.cs.berkeley.edu/~malik/pap...)
[3] Arbelaez, P., Maire, M., Fowlkes, C., & Malik, J. (2011). Contour detection and hierarchical image segmentation.
Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(5), 898-916.
(http://citeseerx.ist.psu.edu/viewdoc/...)
[4] Estrada, F. J., & Jepson, A. D. (2009). Benchmarking image segmentation algorithms.
International Journal of Computer Vision, 85(2), 167-181.
(http://www.cs.toronto.edu/~jepson/seg...)
