Learning to detect natural image boundaries using local brightness, color, and texture cues
Martin, D.R.
Fowlkes, C.C.
Malik, J.
Dept. of Comput. Sci., Boston Coll., Chestnut Hill, MA, USA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: May 2004
Volume: 26,
Issue: 5
On page(s): 530-549
ISSN: 0162-8828
INSPEC Accession Number: 8004312
Digital Object Identifier: 10.1109/TPAMI.2004.1273918
Current Version Published: 2004-03-15
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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