Guided interactive image segmentation using machine learning and color based data set clustering
Year: 2020
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3025770657