We present a novel approach that combines machine learning based interactive image segmentation using supervoxels with a clustering method for the automated identification of similarly colored images in large data sets which enables a guided reuse of classifiers. Our approach solves the problem of significant color variability prevalent and often unavoidable in biological and medical images which typically leads to deteriorated segmentation and quantification accuracy thereby greatly reducing the necessary training effort. This increase in efficiency facilitates the quantification of much larger numbers of images thereby enabling interactive image analysis for recent new technological advances in high-throughput imaging. The presented methods are applicable for almost any image type and represent a useful tool for image analysis tasks in general.

翻译：我们提出了一个新颖的方法,将使用超级蒸汽机学习的交互式图像截面与大型数据集中类似彩色图像自动识别的集群方法结合起来,这样可以对分类器进行引导再利用。我们的方法解决生物和医学图像中普遍存在且往往不可避免的显著的颜色变异性问题,这些问题通常导致分化恶化和量化准确性,从而大大减少必要的培训努力。效率的提高有助于对更多图像进行量化,从而为高通量成像的最新技术进步提供互动图像分析。提出的方法几乎适用于任何类型的图像,是一般图像分析任务的有用工具。