Image segmentation algorithms often depend on appearance models that characterize the distribution of pixel values in different image regions. We describe a new approach for estimating appearance models directly from an image, without explicit consideration of the pixels that make up each region. Our approach is based on novel algebraic expressions that relate local image statistics to the appearance of spatially coherent regions. We describe two algorithms that can use the aforementioned algebraic expressions to estimate appearance models directly from an image. The first algorithm solves a system of linear and quadratic equations using a least squares formulation. The second algorithm is a spectral method based on an eigenvector computation. We present experimental results that demonstrate the proposed methods work well in practice and lead to effective image segmentation algorithms.

翻译：图像分离算法往往取决于不同图像区域像素值分布特征的外观模型。 我们描述一种直接从图像中估算外观模型的新方法,而没有明确考虑构成每个区域的像素。 我们的方法基于新的代数表达式,将本地图像统计与空间一致性区域的外观联系起来。 我们描述两种算法,这两种算法可以使用上述代数表达式直接从图像中估计外观模型。 第一个算法用最小方形的公式解决了线性和二次方形等式系统。 第二个算法是一种光谱法,它以密封基因计算为基础。 我们提出实验结果,表明拟议方法在实践中运作良好,并导致有效的图像分割算法。