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Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
May 2004 (vol. 26 no. 5)
pp. 530-549

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.

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Index Terms:
Texture, supervised learning, cue combination, natural images, ground truth segmentation data set, boundary detection, boundary localization.
Citation:
David R. Martin, Charless C. Fowlkes, Jitendra Malik, "Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 5, pp. 530-549, May 2004, doi:10.1109/TPAMI.2004.1273918
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