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Berkeley Segmentation Data Set and Benchmarks 500 (BSDS500)

Overview

The goal of this work is to provide an empirical basis for research on image segmentation and boundary detection. In order to promote scientific progress in the study of visual grouping, we provide the following resources:

• A large dataset of natural images that have been manually segmented. The human annotations serve as ground truth for learning grouping cues as well as a benchmark for comparing different segmentation and boundary detection algorithms.

• The most recent algorithms our group has developed for contour detection and image segmentation.

• Performance evaluation of the leading computational approaches to grouping.

This is a mirror of the January 2013 update.

If you use the resources in this page, please cite the paper:

Contour Detection and Hierarchical Image Segmentation P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. IEEE TPAMI, Vol. 33, No. 5, pp. 898-916, May 2011. PDF BiBTeX

For more information, please read the original dataset description