# Deep Density-based Image Clustering

arXiv: https://arxiv.org/abs/1812.04287 · HTML: https://mlsanity.com/paper/1812.04287/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2018.

## Built on by (most cited first, 28 of 28)

- [Deep Clustering: A Comprehensive Survey](https://mlsanity.com/paper/2210.04142/) (2022)
- [Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering](https://mlsanity.com/paper/2106.11232/) (2021)
- [Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering](https://mlsanity.com/paper/2103.15069/) (2021)
- [Text Clustering as Classification with LLMs](https://mlsanity.com/paper/2410.00927/) (2024)
- [Information Maximization Clustering via Multi-View Self-Labelling](https://mlsanity.com/paper/2103.07368/) (2021)
- [Multiple Kernel Clustering with Dual Noise Minimization](https://mlsanity.com/paper/2207.06041/) (2022)
- [Non-Linear Fusion for Self-Paced Multi-View Clustering](https://mlsanity.com/paper/2104.09255/) (2021)
- [Selective Pseudo-label Clustering](https://mlsanity.com/paper/2107.10692/) (2021)
- [Combining pretrained CNN feature extractors to enhance clustering of complex natural images](https://mlsanity.com/paper/2101.02767/) (2021)
- [Multi-view MERA Subspace Clustering](https://mlsanity.com/paper/2305.09095/) (2023)
- [Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusters](https://mlsanity.com/paper/2402.00608/) (2024)
- [Dying Clusters Is All You Need -- Deep Clustering With an Unknown Number of Clusters](https://mlsanity.com/paper/2410.09491/) (2024)
- [TDEC: Deep Embedded Image Clustering with Transformer and Distribution Information](https://mlsanity.com/paper/2603.26746/) (2026)
- [UniForCE: The Unimodality Forest Method for Clustering and Estimation of the Number of Clusters](https://mlsanity.com/paper/2312.11323/) (2023)
- [Learning the Precise Feature for Cluster Assignment](https://mlsanity.com/paper/2106.06159/) (2021)
- [Time Series Clustering for Grouping Products Based on Price and Sales Patterns](https://mlsanity.com/paper/2204.08334/) (2022)
- [Density peak clustering using tensor network](https://mlsanity.com/paper/2302.00192/) (2023)
- [Consistent Amortized Clustering via Generative Flow Networks](https://mlsanity.com/paper/2502.19337/) (2025)
- [Separating Boundary Points via Structural Regularization for Very Compact Clusters](https://mlsanity.com/paper/2106.05430/) (2021)
- [fMBN-E: Efficient Unsupervised Network Structure Ensemble and Selection for Clustering](https://mlsanity.com/paper/2107.02071/) (2021)
- [Augmented Data as an Auxiliary Plug-in Towards Categorization of Crowdsourced Heritage Data](https://mlsanity.com/paper/2107.03852/) (2021)
- [TECM: Transfer Learning-based Evidential C-Means Clustering](https://mlsanity.com/paper/2112.10152/) (2021)
- [GCVAE: Generalized-Controllable Variational AutoEncoder](https://mlsanity.com/paper/2206.04225/) (2022)
- [Deep Multi-View Semi-Supervised Clustering with Sample Pairwise Constraints](https://mlsanity.com/paper/2206.04949/) (2022)
- [Transferable Deep Clustering Model](https://mlsanity.com/paper/2310.04946/) (2023)
- [Deep Clustering via Distribution Learning](https://mlsanity.com/paper/2408.03407/) (2024)
- [SHADE: Deep Density-based Clustering](https://mlsanity.com/paper/2410.06265/) (2024)
- [Do Vision Transformers Need All-to-All Attention? Global Communication Through Elastic Learned Cores](https://mlsanity.com/paper/2605.12491/) (2026)

## Abstract (arXiv)

Recently, deep clustering, which is able to perform feature learning that favors clustering tasks via deep neural networks, has achieved remarkable performance in image clustering applications. However, the existing deep clustering algorithms generally need the number of clusters in advance, which is usually unknown in real-world tasks. In addition, the initial cluster centers in the learned feature space are generated by k-means. This only works well on spherical clusters and probably leads to unstable clustering results. In this paper, we propose a two-stage deep density-based image clustering (DDC) framework to address these issues. The first stage is to train a deep convolutional autoencoder (CAE) to extract low-dimensional feature representations from high-dimensional image data, and then apply t-SNE to further reduce the data to a 2-dimensional space favoring density-based clustering algorithms. The second stage is to apply the developed density-based clustering technique on the 2-dimensional embedded data to automatically recognize an appropriate number of clusters with arbitrary shapes. Concretely, a number of local clusters are generated to capture the local structures of clusters, and then are merged via their density relationship to form the final clustering result. Experiments demonstrate that the proposed DDC achieves comparable or even better clustering performance than state-of-the-art deep clustering methods, even though the number of clusters is not given.