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觉中的核心任务之一，解决该问题通常依赖于通过图像各构成区域的颜色分布对其外观数据进行建模。尽管许多分割算法使用交替或隐式方法处理外观模型的依赖性，本文提出一种新方法，无需底层分割的先验信息即可直接从图像估计外观模型。该方法将图像的局部高阶颜色统计信息作为输入，采用基于张量分解的估计器对潜变量模型进行估计。该方法能够对多区域图像中的模型进行估计，并无需用户交互即可自动输出区域比例，克服了先前同类方法的缺陷。我们还通过大量具有挑战性的合成和真实成像场景验证了所提方法的性能，并表明其能够生成高效的分割算法。