Martin David R, Fowlkes Charless C, Malik Jitendra
Computer Science Department, 460 Fulton Hall, Boston College, 140 Commonwealth Ave., Chestnut Hill, MA 02167, USA.
IEEE Trans Pattern Anal Mach Intell. 2004 May;26(5):530-49. doi: 10.1109/TPAMI.2004.1273918.
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.
这项工作的目标是利用局部图像测量准确检测和定位自然场景中的边界。我们制定了对与自然边界相关的亮度、颜色和纹理的特征变化做出响应的特征。为了以最佳方式组合来自这些特征的信息，我们使用人工标注的图像作为真值来训练分类器。该分类器的输出提供了每个图像位置和方向处边界的后验概率。我们给出的精确率-召回率曲线表明，所得检测器显著优于现有方法。我们的两个主要成果是：1）使用简单的线性模型就可以充分进行线索组合；2）要检测自然图像中的边界，需要对纹理进行适当、明确的处理。
