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.

翻译：图像分割是计算机视野的核心任务之一,解决它往往取决于通过每个组成部分的颜色分布模式对图像外观数据进行模型化。虽然许多分解算法使用交替或隐含方法处理外观模型依赖性,但我们在此提出一种新的方法,直接从图像中估算这些模型,而无需事先提供有关基本分解的信息。我们的方法使用图像中的本地高排序颜色统计数据,作为基于分系数的暗中变量模型的输入器。这种方法能够估计多区域图像模型,并在没有用户先前互动的情况下自动输出区域比例,克服先前试图解决这一问题的缺点。我们还展示了我们提出的方法在许多具有挑战性的合成和真实成像情景中的表现,并表明它能够导致高效的分解算法。