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

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Learning to detect natural image boundaries using local brightness, color, and texture cues (2004)

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by David R. Martin , Charless C. Fowlkes , Jitendra Malik

Venue:	PAMI
Citations:	625 - 18 self

BibTeX

@ARTICLE{Martin04learningto,
    author = {David R. Martin and Charless C. Fowlkes and Jitendra Malik},
    title = {Learning to detect natural image boundaries using local brightness, color, and texture cues},
    journal = {PAMI},
    year = {2004},
    volume = {26},
    pages = {530--549}
}

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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.

Keyphrases

natural image boundary texture cue local brightness natural image boundary localization present precision-recall curve ground truth boundary detection optimal way cue combination characteristic change local image measurement image location main result simple linear model ground truth segmentation data set explicit treatment posterior probability index term texture natural scene natural boundary

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