Adaptive Eigenspace Segmentation

TL;DR

This paper introduces an adaptive eigenspace framework for image segmentation that is robust, parameter-free, computationally efficient, and versatile across different images and objects, eliminating the need for tuning or training.

Contribution

It presents a novel adaptive eigenspace method that avoids parameter tuning and training, providing accurate and robust segmentation with reduced computational cost.

Findings

The method yields accurate segmentation results.

It is insensitive to parameter choices.

It operates efficiently without optimization or training.

Abstract

Image segmentation is an inherently ill-posed problem and thus requires regularization in order to limit the search space to reasonable solutions. A majority of segmentation methods integrates these regularization terms in one way or the other in an energy functional using a balancing term. The tuning of this parameter that either favours more the regularization or the data conformity is critical and, unfortunately, the success of the optimization process strongly depends on it. Often the optimal settings change from image to image. In this paper we propose a novel general framework based on an adaptive eigenspace that was first proposed for solving inverse problems. The resulting method proves accurate and yields robust results, without the need for optimization techniques or being sensitive to the parameter choice. In fact, the method solves a symmetric positive definite sparse system and hence, uses only a fraction of the computational cost. The method is very versatile and does not need parameter-tuning, when segmenting objects from any kind of an image or when segmenting different organs. As the adaptive eigenspace is determined directly from the image to segment, the approach also does not need a tedious training phase.