Learning to detect natural image boundaries using local brightness, color, and texture cues

Abstract

The goal of this work is to accurately detect and localize boundaries in natural scenes using local image measurements. We formulate features that respond to characteristic changes in brightness, color, and texture associated with natural boundaries. In order to combine the information from these features in an optimal way, we train a classifier using human labeled images as ground truth. The output of this classifier provides the posterior probability of a boundary at each image location and orientation. We present precision-recall curves showing that the resulting detector significantly outperforms existing approaches. Our two main results are 1) that cue combination can be performed adequately with a simple linear model and 2) that a proper, explicit treatment of texture is required to detect boundaries in natural images.

DOI: 10.1109/TPAMI.2004.1273918
View Slides

Extracted Key Phrases

17 Figures and Tables

0100200'04'06'08'10'12'14'16
Citations per Year

1,853 Citations

Semantic Scholar estimates that this publication has 1,853 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Martin2004LearningTD, title={Learning to detect natural image boundaries using local brightness, color, and texture cues}, author={David R. Martin and Charless C. Fowlkes and Jitendra Malik}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, year={2004}, volume={26}, pages={530-549} }