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A class hierarchy approach for the segmentation of natural scenes

机译:自然场景分割的级别层次方法

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Scene segmentation techniques normally model scene content in terms of a set of mutually-exclusive classes. This model is used both by local feature classifiers and by later stages of processing such as relaxation labelling. This paper generalises these schemes to a hierarchical class model where the labelling of each pixel is the result of a path down the class hierarchy. The benefits of the hierarchical scheme are discussed, and an efficient relaxation labelling algorithm for the class hierarchy is given. The technique is demonstrated on synthetic textured images with added noise and a comparison in terms of performance and complexity is made with the traditional model.
机译:场景分割技术通常在一组相互专用类方面模拟场景内容。该模型由本地特征分类器和稍后的处理阶段使用,例如放松标签。本文将这些方案概括为分层类模型,其中每个像素的标签是类层次结构的路径的结果。讨论了分层方案的益处,给出了类层次结构的有效放松标记算法。该技术在具有额外的噪声的合成纹理图像上进行说明,并且在性能和复杂性方面的比较是用传统模型进行的。

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