Unsupervised Deep Learning Meets Chan-Vese Model
- URL: http://arxiv.org/abs/2204.06951v1
- Date: Thu, 14 Apr 2022 13:23:57 GMT
- Title: Unsupervised Deep Learning Meets Chan-Vese Model
- Authors: Dihan Zheng, Chenglong Bao, Zuoqiang Shi, Haibin Ling, Kaisheng Ma
- Abstract summary: We propose an unsupervised image segmentation approach that integrates the Chan-Vese (CV) model with deep neural networks.
Our basic idea is to apply a deep neural network that maps the image into a latent space to alleviate the violation of the piecewise constant assumption in image space.
- Score: 77.24463525356566
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Chan-Vese (CV) model is a classic region-based method in image
segmentation. However, its piecewise constant assumption does not always hold
for practical applications. Many improvements have been proposed but the issue
is still far from well solved. In this work, we propose an unsupervised image
segmentation approach that integrates the CV model with deep neural networks,
which significantly improves the original CV model's segmentation accuracy. Our
basic idea is to apply a deep neural network that maps the image into a latent
space to alleviate the violation of the piecewise constant assumption in image
space. We formulate this idea under the classic Bayesian framework by
approximating the likelihood with an evidence lower bound (ELBO) term while
keeping the prior term in the CV model. Thus, our model only needs the input
image itself and does not require pre-training from external datasets.
Moreover, we extend the idea to multi-phase case and dataset based unsupervised
image segmentation. Extensive experiments validate the effectiveness of our
model and show that the proposed method is noticeably better than other
unsupervised segmentation approaches.
Related papers
- UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation [64.01742988773745]
An increasing privacy concern exists regarding training large-scale image segmentation models on unauthorized private data.
We exploit the concept of unlearnable examples to make images unusable to model training by generating and adding unlearnable noise into the original images.
We empirically verify the effectiveness of UnSeg across 6 mainstream image segmentation tasks, 10 widely used datasets, and 7 different network architectures.
arXiv Detail & Related papers (2024-10-13T16:34:46Z) - Hybrid diffusion models: combining supervised and generative pretraining for label-efficient fine-tuning of segmentation models [55.2480439325792]
We propose a new pretext task, which is to perform simultaneously image denoising and mask prediction on the first domain.
We show that fine-tuning a model pretrained using this approach leads to better results than fine-tuning a similar model trained using either supervised or unsupervised pretraining.
arXiv Detail & Related papers (2024-08-06T20:19:06Z) - FreeSeg-Diff: Training-Free Open-Vocabulary Segmentation with Diffusion Models [56.71672127740099]
We focus on the task of image segmentation, which is traditionally solved by training models on closed-vocabulary datasets.
We leverage different and relatively small-sized, open-source foundation models for zero-shot open-vocabulary segmentation.
Our approach (dubbed FreeSeg-Diff), which does not rely on any training, outperforms many training-based approaches on both Pascal VOC and COCO datasets.
arXiv Detail & Related papers (2024-03-29T10:38:25Z) - Hierarchical Uncertainty Estimation for Medical Image Segmentation
Networks [1.9564356751775307]
Uncertainty exists in both images (noise) and manual annotations (human errors and bias) used for model training.
We propose a simple yet effective method for estimating uncertainties at multiple levels.
We demonstrate that a deep learning segmentation network such as U-net, can achieve a high segmentation performance.
arXiv Detail & Related papers (2023-08-16T16:09:23Z) - A Deep Active Contour Model for Delineating Glacier Calving Fronts [17.061463565692456]
Recent studies have shown that combining segmentation with edge detection can improve the accuracy of calving front detectors.
We propose a model for explicit contour detection that does not incorporate any dense predictions as intermediate steps.
The proposed approach, called Charting Outlines by Recurrent Adaptation'' (COBRA), combines Convolutional Neural Networks (CNNs) for feature extraction and active contour models for the delineation.
arXiv Detail & Related papers (2023-07-07T08:45:46Z) - Estimating Appearance Models for Image Segmentation via Tensor
Factorization [0.0]
We propose a new approach to directly estimate appearance models from the image without prior information on the underlying segmentation.
Our method uses local high order color statistics from the image as an input to tensor factorization-based estimator for latent variable models.
This approach is able to estimate models in multiregion images and automatically output the regions proportions without prior user interaction.
arXiv Detail & Related papers (2022-08-16T17:21:00Z) - Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation
Models [0.0]
We propose a new co-training process for synth-to-real UDA of semantic segmentation models.
Our co-training shows improvements of 15-20 percentage points of mIoU over baselines.
arXiv Detail & Related papers (2022-05-31T13:30:36Z) - Meta Internal Learning [88.68276505511922]
Internal learning for single-image generation is a framework, where a generator is trained to produce novel images based on a single image.
We propose a meta-learning approach that enables training over a collection of images, in order to model the internal statistics of the sample image more effectively.
Our results show that the models obtained are as suitable as single-image GANs for many common image applications.
arXiv Detail & Related papers (2021-10-06T16:27:38Z) - Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation [49.90178055521207]
This work addresses weakly supervised semantic segmentation (WSSS), with the goal of bridging the gap between image-level annotations and pixel-level segmentation.
We formulate WSSS as a novel group-wise learning task that explicitly models semantic dependencies in a group of images to estimate more reliable pseudo ground-truths.
In particular, we devise a graph neural network (GNN) for group-wise semantic mining, wherein input images are represented as graph nodes.
arXiv Detail & Related papers (2020-12-09T12:40:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.