论文标题
通过张量分解图像分割的估计外观模型
Estimating Appearance Models for Image Segmentation via Tensor Factorization
论文作者
论文摘要
图像分割是计算机视觉中的核心任务之一，求解它通常取决于通过其组成区域的颜色分布对图像外观数据进行建模。尽管许多细分算法使用交替或隐式方法处理外观模型依赖性，但我们在这里提出了一种新方法，可以直接从图像估算它们，而无需先前有关基础分割的信息。我们的方法使用来自图像的局部高阶颜色统计信息作为潜在变量模型的基于张量分解的估计器的输入。这种方法能够估计多射线图像中的模型，并在没有用户交互的情况下自动输出区域比例，从而克服了从事先尝试到此问题的缺点。我们还在许多具有挑战性的合成和真实成像方案中证明了我们提出的方法的性能，并表明它导致了有效的分割算法。
Image Segmentation is one of the core tasks in Computer Vision and solving it often depends on modeling the image appearance data via the color distributions of each it its constituent regions. Whereas many segmentation algorithms handle the appearance models dependence using alternation or implicit methods, we propose here a new approach to directly estimate them from the image without prior information on the underlying segmentation. Our method uses local high order color statistics from the image as an input to tensor factorization-based estimator for latent variable models. This approach is able to estimate models in multiregion images and automatically output the regions proportions without prior user interaction, overcoming the drawbacks from a prior attempt to this problem. We also demonstrate the performance of our proposed method in many challenging synthetic and real imaging scenarios and show that it leads to an efficient segmentation algorithm.
