Unsupervised Few-Shot Image Segmentation with Dense Feature Learning and Sparse Clustering

Kuangdai Leng, Robert Atwood, Winfried Kockelmann, Deniza Chekrygina, Jeyan Thiyagalingam

2024

Abstract

Fully unsupervised semantic segmentation of images has been a challenging problem in computer vision. Many deep learning models have been developed for this task, most of which using representation learning guided by certain unsupervised or self-supervised loss functions towards segmentation. In this paper, we conduct dense or pixel-level representation learning using a fully-convolutional autoencoder; the learned dense features are then reduced onto a sparse graph where segmentation is encouraged from three aspects: nor-malised cut, similarity and continuity. Our method is one- or few-shot, minimally requiring only one image (i.e., the target image). To mitigate overfitting caused by few-shot learning, we compute the reconstruction loss using augmented size-varying patches sampled from the image(s). We also propose a new adjacency-based loss function for continuity, which allows the number of superpixels to be arbitrarily large whereby the creation of the sparse graph can remain fully unsupervised. We conduct quantitative and qualitative experiments using computer vision images and videos, which show that segmentation becomes more accurate and robust using our sparse loss functions and patch reconstruction. For comprehensive application, we use our method to analyse 3D images acquired from X-ray and neutron tomography. These experiments and applications show that our model trained with one or a few images can be highly robust for predicting many unseen images with similar semantic contents; therefore, our method can be useful for the segmentation of videos and 3D images of this kind with lightweight model training in 2D.

Paper Citation

in Harvard Style

Leng K., Atwood R., Kockelmann W., Chekrygina D. and Thiyagalingam J. (2024). Unsupervised Few-Shot Image Segmentation with Dense Feature Learning and Sparse Clustering. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 575-586. DOI: 10.5220/0012380700003660

in Bibtex Style

@conference{visapp24,
author={Kuangdai Leng and Robert Atwood and Winfried Kockelmann and Deniza Chekrygina and Jeyan Thiyagalingam},
title={Unsupervised Few-Shot Image Segmentation with Dense Feature Learning and Sparse Clustering},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={575-586},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012380700003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Unsupervised Few-Shot Image Segmentation with Dense Feature Learning and Sparse Clustering
SN - 978-989-758-679-8
AU - Leng K.
AU - Atwood R.
AU - Kockelmann W.
AU - Chekrygina D.
AU - Thiyagalingam J.
PY - 2024
SP - 575
EP - 586
DO - 10.5220/0012380700003660
PB - SciTePress