We present a novel approach that combines machine learning based interactive image segmentation with a two-stage clustering method to identify similarly colored images for efficient batch image segmentation by guided reuse of classifiers. The segmentation task is formulated as a supervised machine learning problem working on homogeneous groups of voxels termed supervoxels. Classifiers are interactively trained from sparse annotations in an iterative process of annotation refinement. Resulting models can be used for batch processing of previously unseen images. By clustering images into subsets of similar colorization, we identify a minimal set of prototype images and demonstrate that using only classifiers trained on these prototype images for their color-cluster significantly improves the average segmentation performance of batch processing. The presented methods are applicable for almost any image type and therefore represent a useful tool for image analysis tasks in general.

翻译：我们提出了一个新颖的方法,将基于机器学习的交互式图像分割与两阶段集成法结合起来,通过对分类器进行引导再利用,为高效批量图像分解确定相似的彩色图像。分解任务被设计成一个在同质氧化物类中工作的受监督的机器学习问题。分类器在迭代注解精细化过程中从稀疏的注解中进行互动培训。由此产生的模型可用于对先前不为人知的图像进行分批处理。通过将图像分组成相近的颜色分集,我们确定了一套最起码的原型图像,并表明仅使用经过有关这些原型图像培训的分解器对其颜色分组进行分解,就能大大提高批处理的平均分解性。所提出的方法几乎适用于任何图像类型,因此是一般图像分析任务的有用工具。