Content-based Propagation of User Markings for Interactive Segmentation of Patterned Images

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Abstract

Efficient and easy segmentation of images and volumes is of great practical importance. Segmentation problems which motivate our approach originate from imaging commonly used in materials science and medicine. We formulate image segmentation as a probabilistic pixel classification problem, and we apply segmentation as a step towards characterising image content. Our method allows the user to define structures of interest by interactively marking a subset of pixels. Thanks to the real-time feedback, the user can place new markings strategically, depending on the current outcome. The final pixel classification may be obtained from a very modest user input. An important ingredient of our method is a graph that encodes image content. This graph is built in an unsupervised manner during initialisation, and is based on clustering of image features. Since we combine a limited amount of user-labelled data with the clustering information obtained from the unlabelled parts of the image, our method fits in the general framework of semi-supervised learning. We demonstrate how this can be a very efficient approach to segmentation through pixel classification.
Original languageEnglish
Title of host publicationProceedings of Computer Vision for Microscopy Image Analysis (CVMI) workshop
PublisherIEEE
Publication date2020
Pages4280-4288
Article number35
ISBN (Print)978-1-7281-9361-8
DOIs
Publication statusPublished - 2020
EventWorkshop on Computer Vision for Microscopy Image Analysis: held in conjunction with the CVPR 2020 - Virtual
Duration: 19 Jun 202019 Jun 2020
https://cvmi2020.github.io/index.html

Workshop

WorkshopWorkshop on Computer Vision for Microscopy Image Analysis
LocationVirtual
Period19/06/202019/06/2020
Internet address

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