Variational multichannel multiclass segmentation using unsupervised lifting with CNNs

TL;DR

This paper introduces an unsupervised segmentation method combining variational energy models and CNNs, capable of dividing images into multiple regions without supervision, effective on diverse image types.

Contribution

It presents a novel unsupervised multiclass segmentation approach integrating a multichannel variational model with CNNs for feature extraction.

Findings

Effective segmentation of various image types including textures and medical images.

Comparable or improved performance over existing multiphase segmentation methods.

Demonstrates the utility of combining variational models with deep learning for unsupervised segmentation.

Abstract

We propose an unsupervised image segmentation approach, that combines a variational energy functional and deep convolutional neural networks. The variational part is based on a recent multichannel multiphase Chan-Vese model, which is capable to extract useful information from multiple input images simultaneously. We implement a flexible multiclass segmentation method that divides a given image into $K$ different regions. We use convolutional neural networks (CNNs) targeting a pre-decomposition of the image. By subsequently minimising the segmentation functional, the final segmentation is obtained in a fully unsupervised manner. Special emphasis is given to the extraction of informative feature maps serving as a starting point for the segmentation. The initial results indicate that the proposed method is able to decompose and segment the different regions of various types of images, such as texture and medical images and compare its performance with another multiphase segmentation method.