Interactive Image Segmentation: From Graph-based Algorithms to Feature-Space Annotation

Abstract

In recent years, machine learning algorithms that solve problems from a collection of examples (i.e. labeled data), have grown to be the predominant approach for solving computer vision and image processing tasks. These algorithms’ performance is highly correlated with the abundance of examples and their quality, especially methods based on neural networks, which are significantly data-hungry. Notably, image segmentation annotation requires extensive effort to produce high-quality labeling due to the fine-scale of the units (pixels) and resorts to interactive methodologies to provide user assistance. Therefore, improving interactive image segmentation methodologies with the goal of improving data labeling problems is of paramount importance to advance applications of computer vision methods. With this in mind, we investigated the existing literature on interactive image segmentation, contributing to it by introducing novel algorithms that perform the segmentation from markers, contours, and finally proposing a new paradigm for image annotation at scale.

Image Segmentation and Grouping Using Graph Method (SLIC Algorithm)

“…The use of interactive image segmentation is growing quickly in several fields like image processing, computer vision and medicine. As mentioned by Bragantini and Falcao (2022), graph-based segmentation (GBS) methods are commonly used for feature-based annotation. Similarly, Saha, Bajger, and Lee (2018) proposed using GBS to efficiently segment cell nuclei in medical images.…”