Papers Unsupervised Image Segmentation
“Unsupervised Image Segmentation” 태그가 달린 논문 61편 · 필터 해제
Unsupervised Image Segmentation using Mutual Mean-Teaching
Unsupervised image segmentation aims at assigning the pixels with similar feature into a same cluster without annotation, which is an important task in computer vision. Due to lack of prior knowledge, most of existing mo…
Image SegmentationSegmentationSemantic SegmentationUnsupervised Image SegmentationInformation-Theoretic Segmentation by Inpainting Error Maximization
We study image segmentation from an information-theoretic perspective, proposing a novel adversarial method that performs unsupervised segmentation by partitioning images into maximally independent sets. More specificall…
Image SegmentationSegmentationSemantic SegmentationUnsupervised Image SegmentationUnsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering
The usage of convolutional neural networks (CNNs) for unsupervised image segmentation was investigated in this study. In the proposed approach, label prediction and network parameter learning are alternately iterated to …
ClusteringImage SegmentationSegmentationSemantic Segmentation+1Autoregressive Unsupervised Image Segmentation
In this work, we propose a new unsupervised image segmentation approach based on mutual information maximization between different constructed views of the inputs. Taking inspiration from autoregressive generative models…
ClusteringImage SegmentationRepresentation LearningSegmentation+4GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
Generative latent-variable models are emerging as promising tools in robotics and reinforcement learning. Yet, even though tasks in these domains typically involve distinct objects, most state-of-the-art generative model…
Image GenerationObject DiscoveryReinforcement LearningRepresentation Learning+3Flexibly Regularized Mixture Models and Application to Image Segmentation
Probabilistic finite mixture models are widely used for unsupervised clustering. These models can often be improved by adapting them to the topology of the data. For instance, in order to classify spatially adjacent data…
ClusteringImage SegmentationSemantic SegmentationUnsupervised Image SegmentationSalient object detection on hyperspectral images using features learned from unsupervised segmentation task
Various saliency detection algorithms from color images have been proposed to mimic eye fixation or attentive object detection response of human observers for the same scenes. However, developments on hyperspectral imagi…
ClusteringImage SegmentationObjectobject-detection+6Consistent estimation of the max-flow problem: Towards unsupervised image segmentation
Advances in the image-based diagnostics of complex biological and manufacturing processes have brought unsupervised image segmentation to the forefront of enabling automated, on the fly decision making. However, most exi…
Brain Tumor SegmentationDecision MakingImage SegmentationSegmentation+2Unsupervised learning of foreground object detection
Unsupervised learning poses one of the most difficult challenges in computer vision today. The task has an immense practical value with many applications in artificial intelligence and emerging technologies, as large qua…
Image SegmentationObjectobject-detectionObject Detection+5W-Net: A Deep Model for Fully Unsupervised Image Segmentation
While significant attention has been recently focused on designing supervised deep semantic segmentation algorithms for vision tasks, there are many domains in which sufficient supervised pixel-level labels are difficult…
Image SegmentationSegmentationSemantic SegmentationUnsupervised Image SegmentationA First Derivative Potts Model for Segmentation and Denoising Using ILP
Unsupervised image segmentation and denoising are two fundamental tasks in image processing. Usually, graph based models such as multicut are used for segmentation and variational models are employed for denoising. Our a…
DenoisingImage SegmentationSegmentationSemantic Segmentation+1Unsupervised Image Segmentation using the Deffuant-Weisbuch Model from Social Dynamics
Unsupervised image segmentation algorithms aim at identifying disjoint homogeneous regions in an image, and have been subject to considerable attention in the machine vision community. In this paper, a popular theoretica…
Image SegmentationSegmentationSemantic SegmentationUnsupervised Image SegmentationVoronoi Region-Based Adaptive Unsupervised Color Image Segmentation
Color image segmentation is a crucial step in many computer vision and pattern recognition applications. This article introduces an adaptive and unsupervised clustering approach based on Voronoi regions, which can be app…
ClusteringImage SegmentationSegmentationSemantic Segmentation+1A regularization-based approach for unsupervised image segmentation
We propose a novel unsupervised image segmentation algorithm, which aims to segment an image into several coherent parts. It requires no user input, no supervised learning phase and assumes an unknown number of segments.…
Image SegmentationSemantic SegmentationSuperpixelsUnsupervised Image SegmentationA Critical Connectivity Radius for Segmenting Randomly-Generated, High Dimensional Data Points
Motivated by a $2$-dimensional (unsupervised) image segmentation task whereby local regions of pixels are clustered via edge detection methods, a more general probabilistic mathematical framework is devised. Critical thr…
Edge DetectionImage SegmentationSemantic SegmentationUnsupervised Image SegmentationBayesian nonparametric image segmentation using a generalized Swendsen-Wang algorithm
Unsupervised image segmentation aims at clustering the set of pixels of an image into spatially homogeneous regions. We introduce here a class of Bayesian nonparametric models to address this problem. These models are ba…
ClusteringImage SegmentationSegmentationSemantic Segmentation+1Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications
We investigate two novel mixed robust/average-case submodular data partitioning problems that we collectively call Submodular Partitioning. These problems generalize purely robust instances of the problem, namely max-min…
ClusteringDistributed OptimizationImage SegmentationSemantic Segmentation+1Unsupervised image segmentation by Global and local Criteria Optimization Based on Bayesian Networks
Today Bayesian networks are more used in many areas of decision support and image processing. In this way, our proposed approach uses Bayesian Network to modelize the segmented image quality. This quality is calculated o…
Image SegmentationSegmentationSemantic SegmentationSuperpixels+1Cut, Glue & Cut: A Fast, Approximate Solver for Multicut Partitioning
Recently, unsupervised image segmentation has become increasingly popular. Starting from a superpixel segmentation, an edge-weighted region adjacency graph is constructed. Amongst all segmentations of the graph, the one …
Image SegmentationSemantic SegmentationUnsupervised Image SegmentationSpatial distance dependent Chinese restaurant processes for image segmentation
The distance dependent Chinese restaurant process (ddCRP) was recently introduced to accommodate random partitions of non-exchangeable data. The ddCRP clusters data in a biased way: each data point is more likely to be …
Image SegmentationSegmentationSemantic SegmentationUnsupervised Image Segmentation