论文标题
使用机器学习和基于颜色的数据集群集的指导交互式图像分割
Guided interactive image segmentation using machine learning and color based data set clustering
论文作者
论文摘要
我们提出了一种新颖的方法，该方法将基于机器学习的交互式图像分割结合在一起，使用Supersoxels与聚类方法相结合，用于在大型数据集中自动识别类似彩色图像的自动识别，从而使分类器的指导重复使用。我们的方法解决了普遍的颜色可变性的问题，并且在生物学和医学图像中通常不可避免，这通常会导致分割恶化和量化精度，从而大大降低了必要的训练工作。效率的提高有助于量化大量图像，从而为高通量成像中的最新技术进步提供了交互式图像分析。呈现的方法几乎适用于任何图像类型，并代表了图像分析任务的有用工具。
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.
