TDEC: Deep Embedded Image Clustering with Transformer and Distribution Information

• URL: http://arxiv.org/abs/2603.26746v1
• Date: Mon, 23 Mar 2026 08:46:42 GMT
• Title: TDEC: Deep Embedded Image Clustering with Transformer and Distribution Information
• Abstract summary: We propose a deep embedded image clustering method called TDEC.<n>TDEC considers feature representation, dimensional preference, and robust assignment for image clustering.<n>Our method is robust and allows for more flexibility in data size, the number of clusters, and the context complexity.
• Score: 10.865555707958057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image clustering is a crucial but challenging task in multimedia machine learning. Recently the combination of clustering with deep learning has achieved promising performance against conventional methods on high-dimensional image data. Unfortunately, existing deep clustering methods (DC) often ignore the importance of information fusion with a global perception field among different image regions on clustering images, especially complex ones. Additionally, the learned features are usually clustering-unfriendly in terms of dimensionality and are based only on simple distance information for the clustering. In this regard, we propose a deep embedded image clustering TDEC, which for the first time to our knowledge, jointly considers feature representation, dimensional preference, and robust assignment for image clustering. Specifically, we introduce the Transformer to form a novel module T-Encoder to learn discriminative features with global dependency while using the Dim-Reduction block to build a friendly low-dimensional space favoring clustering. Moreover, the distribution information of embedded features is considered in the clustering process to provide reliable supervised signals for joint training. Our method is robust and allows for more flexibility in data size, the number of clusters, and the context complexity. More importantly, the clustering performance of TDEC is much higher than recent competitors. Extensive experiments with state-of-the-art approaches on complex datasets show the superiority of TDEC.

Related papers

• Deep Image Clustering Based on Curriculum Learning and Density Information [10.865555707958057]
  This paper introduces a model training strategy using density information into image clustering.<n>Specifically, we design a curriculum learning scheme grounded in the density information of input data, with a more reasonable learning pace.<n>Extensive comparisons with state-of-the-art clustering approaches on benchmark datasets demonstrate the superiority of the proposed method.
  arXiv  Detail & Related papers  (2026-03-31T02:54:21Z)
• Deep Clustering with Associative Memories [28.46602918457589]
  We propose a novel loss function utilizing energy-based dynamics via Associative Memories to formulate a new deep clustering method, DCAM.<n>Our experiments showcase the advantage of DCAM, producing improved clustering quality for various architecture choices.
  arXiv  Detail & Related papers  (2026-01-02T19:21:06Z)
• Hierarchical Identity Learning for Unsupervised Visible-Infrared Person Re-Identification [81.3063589622217]
  Unsupervised visible-infrared person re-identification (USVI-ReID) aims to learn modality-invariant image features from unlabeled cross-modal person datasets.
  arXiv  Detail & Related papers  (2025-09-15T05:10:43Z)
• Self-Enhanced Image Clustering with Cross-Modal Semantic Consistency [57.961869351897384]
  We propose a framework based on cross-modal semantic consistency for efficient image clustering.<n>Our framework first builds a strong foundation via Cross-Modal Semantic Consistency.<n>In the first stage, we train lightweight clustering heads to align with the rich semantics of the pre-trained model.<n>In the second stage, we introduce a Self-Enhanced fine-tuning strategy.
  arXiv  Detail & Related papers  (2025-08-02T08:12:57Z)
• Structural-Spectral Graph Convolution with Evidential Edge Learning for Hyperspectral Image Clustering [73.01141916544103]
  Hyperspectral image (HSI) clustering groups pixels into clusters without labeled data.<n>Most methods rely on superpixel segmentation and perform superpixel-level clustering based on graph neural networks (GNNs)<n>We propose a structural-spectral graph convolutional operator (SSGCO) tailored for graph-structured HSI superpixels to improve representation quality.
  arXiv  Detail & Related papers  (2025-06-11T16:41:34Z)
• Image Clustering with External Guidance [33.664812922814754]
  The core of clustering is incorporating prior knowledge to construct supervision signals. We propose leveraging external knowledge as a new supervision signal to guide clustering, even though it seems irrelevant to the given data.
  arXiv  Detail & Related papers  (2023-10-18T14:20:55Z)
• Deep Image Clustering with Contrastive Learning and Multi-scale Graph Convolutional Networks [58.868899595936476]
  This paper presents a new deep clustering approach termed image clustering with contrastive learning and multi-scale graph convolutional networks (IcicleGCN) Experiments on multiple image datasets demonstrate the superior clustering performance of IcicleGCN over the state-of-the-art.
  arXiv  Detail & Related papers  (2022-07-14T19:16:56Z)
• DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks [53.88811980967342]
  This paper presents a Deep Clustering via Ensembles (DeepCluE) approach. It bridges the gap between deep clustering and ensemble clustering by harnessing the power of multiple layers in deep neural networks. Experimental results on six image datasets confirm the advantages of DeepCluE over the state-of-the-art deep clustering approaches.
  arXiv  Detail & Related papers  (2022-06-01T09:51:38Z)
• Clustering by Maximizing Mutual Information Across Views [62.21716612888669]
  We propose a novel framework for image clustering that incorporates joint representation learning and clustering. Our method significantly outperforms state-of-the-art single-stage clustering methods across a variety of image datasets.
  arXiv  Detail & Related papers  (2021-07-24T15:36:49Z)
• Very Compact Clusters with Structural Regularization via Similarity and Connectivity [3.779514860341336]
  We propose an end-to-end deep clustering algorithm, i.e., Very Compact Clusters (VCC) for the general datasets. Our proposed approach achieves better clustering performance over most of the state-of-the-art clustering methods.
  arXiv  Detail & Related papers  (2021-06-09T23:22:03Z)
• Deep Transformation-Invariant Clustering [24.23117820167443]
  We present an approach that does not rely on abstract features but instead learns to predict image transformations. This learning process naturally fits in the gradient-based training of K-means and Gaussian mixture model. We demonstrate that our novel approach yields competitive and highly promising results on standard image clustering benchmarks.
  arXiv  Detail & Related papers  (2020-06-19T13:43:08Z)

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.