# Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering

arXiv: https://arxiv.org/abs/2007.09990 · HTML: https://mlsanity.com/paper/2007.09990/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 30 of 61)

- [TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation](https://mlsanity.com/paper/2112.01515/) (2021)
- [Semantic Segmentation In-the-Wild Without Seeing Any Segmentation Examples](https://mlsanity.com/paper/2112.03185/) (2021)
- [Unsupervised Multi-View Object Segmentation Using Radiance Field Propagation](https://mlsanity.com/paper/2210.00489/) (2022)
- [Locally Stylized Neural Radiance Fields](https://mlsanity.com/paper/2309.10684/) (2023)
- [Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations](https://mlsanity.com/paper/2303.16891/) (2023)
- [Unsupervised Object Localization in the Era of Self-Supervised ViTs: A Survey](https://mlsanity.com/paper/2310.12904/) (2023)
- [A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends](https://mlsanity.com/paper/2402.15490/) (2024)
- [A Novel CNN-LSTM-based Approach to Predict Urban Expansion](https://mlsanity.com/paper/2103.01695/) (2021)
- [Q-Seg: Quantum Annealing-Based Unsupervised Image Segmentation](https://mlsanity.com/paper/2311.12912/) (2023)
- [On-Device Unsupervised Image Segmentation](https://mlsanity.com/paper/2303.12753/) (2023)
- [CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation](https://mlsanity.com/paper/2209.11359/) (2022)
- [Learning Foreground-Background Segmentation from Improved Layered GANs](https://mlsanity.com/paper/2104.00483/) (2021)
- [GOSS: Towards Generalized Open-set Semantic Segmentation](https://mlsanity.com/paper/2203.12116/) (2022)
- [Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs](https://mlsanity.com/paper/2309.01899/) (2023)
- [Unsupervised Tissue Segmentation via Deep Constrained Gaussian Network](https://mlsanity.com/paper/2208.02912/) (2022)
- [ONeRF: Unsupervised 3D Object Segmentation from Multiple Views](https://mlsanity.com/paper/2211.12038/) (2022)
- [TransAdapt: A Transformative Framework for Online Test Time Adaptive Semantic Segmentation](https://mlsanity.com/paper/2302.14611/) (2023)
- [Estimation of control area in badminton doubles with pose information from top and back view drone videos](https://mlsanity.com/paper/2305.04247/) (2023)
- [Self-Supervised Leaf Segmentation under Complex Lighting Conditions](https://mlsanity.com/paper/2203.15943/) (2022)
- [Foreground-Background Separation through Concept Distillation from Generative Image Foundation Models](https://mlsanity.com/paper/2212.14306/) (2022)
- [Deep Neural Networks Learn Meta-Structures from Noisy Labels in Semantic Segmentation](https://mlsanity.com/paper/2103.11594/) (2021)
- [Topology-Preserving Segmentation Network: A Deep Learning Segmentation Framework for Connected Component](https://mlsanity.com/paper/2202.13331/) (2022)
- [Guided deep learning by subaperture decomposition: ocean patterns from SAR imagery](https://mlsanity.com/paper/2204.04438/) (2022)
- [K-textures, a self-supervised hard clustering deep learning algorithm for satellite image segmentation](https://mlsanity.com/paper/2205.08671/) (2022)
- [Image Segmentation-based Unsupervised Multiple Objects Discovery](https://mlsanity.com/paper/2212.10124/) (2022)
- [DynaSeg: A Deep Dynamic Fusion Method for Unsupervised Image Segmentation Incorporating Feature Similarity and Spatial Continuity](https://mlsanity.com/paper/2405.05477/) (2024)
- [Segmentation by Factorization: Unsupervised Semantic Segmentation for Pathology by Factorizing Foundation Model Features](https://mlsanity.com/paper/2409.05697/) (2024)
- [AMNCutter: Affinity-Attention-Guided Multi-View Normalized Cutter for Unsupervised Surgical Instrument Segmentation](https://mlsanity.com/paper/2411.03695/) (2024)
- [Multi-source Pseudo-label Learning of Semantic Segmentation for the Scene Recognition of Agricultural Mobile Robots](https://mlsanity.com/paper/2102.06386/) (2021)
- [Pixel-Level Clustering Network for Unsupervised Image Segmentation](https://mlsanity.com/paper/2310.16234/) (2023)

## Abstract (arXiv)

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 meet the following criteria: (a) pixels of similar features should be assigned the same label, (b) spatially continuous pixels should be assigned the same label, and (c) the number of unique labels should be large. Although these criteria are incompatible, the proposed approach minimizes the combination of similarity loss and spatial continuity loss to find a plausible solution of label assignment that balances the aforementioned criteria well. The contributions of this study are four-fold. First, we propose a novel end-to-end network of unsupervised image segmentation that consists of normalization and an argmax function for differentiable clustering. Second, we introduce a spatial continuity loss function that mitigates the limitations of fixed segment boundaries possessed by previous work. Third, we present an extension of the proposed method for segmentation with scribbles as user input, which showed better accuracy than existing methods while maintaining efficiency. Finally, we introduce another extension of the proposed method: unseen image segmentation by using networks pre-trained with a few reference images without re-training the networks. The effectiveness of the proposed approach was examined on several benchmark datasets of image segmentation.