Image Clustering

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2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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LoomSC: Scalable Deep Subspace Clustering with Projector Factorization and Exact Spectral Reduction
Oct 7, 2026Nairouz Mrabah, Youssef Melki, Mohamed Bouguessa +3Clustering Spectral Clustering
École de technologie supérieure (ÉTS), Montréal, QC, Canada · University of Bahrain (UOB), Kingdom of Bahrain · Université du Québec à Montréal (UQAM), Montréal, QC, Canada +1
VLM4Cluster: Benchmarking Deep Clustering In the Era of Vision-Language Pre-training
Sep 29, 2026Yuanwei Hu, Bo Peng, Yuheng Jia +3Clustering Deep Clustering
University College London · The University of Queensland · Southeast University +2
InfoTaxa: Information-Calibrated Label-Free Clustering for Fine-Grained Visual Taxonomy
Sep 15, 2026David Ahmedt-Aristizabal, Mohammad Ali Armin, Lars PeterssonVisual Representation Learning Clustering
Imaging and Computer Vision Group, CSIRO, Australia
VeriCam: A Verification Baseline for the Classification of Unknown Data
Aug 31, 2026Lucas Wojcik, Gabriel E. Lima, Sergio M. Silva +2Domain Generalization Image Clustering
Department of Informatics, Federal University of Paran´a, Curitiba, Brazil · Department of Technological Development and Quality, Paran´a Military Police, Curitiba, Brazil
Learning Deep Modality-Shared Self-Expressiveness for Image Clustering with Textual Information
Aug 9, 2026Xianghan Meng, Wei He, Zhiyuan Huang +1Cross-Modal Representation Learning Clustering
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, P.R. China
Calibrated Similarity and Graph Clustering for Open-Set Animal Re-Identification
Aug 3, 2026Mohamed ElBassat, Seifeldin Elkerdany, Mohamed ElBialy +5Animal Re-Identification Image Matching
Made In Alexandria Artificial Intelligence Team, Alexandria, Egypt · Faculty of Computers and Data Science, Alexandria University, Alexandria, Egypt · Faculty of Computer Science and Engineering, Alamein International University, New Alamein City, 51718, Egypt +3
Converge to Surprise: Evolutionary Self-supervised Image Clustering
Jul 8, 2026Canlin Zhang, Xiuwen LiuUnsupervised Learning Self-Supervised Learning
Independent Researcher · Department of Computer Science Florida State University
Universal Guideline-Driven Image Clustering via a Hybrid LLM Agent
Jun 23, 2026Wenliang Zhong, Rob Barton, Lucas Goncalves +7Clustering Unsupervised Clustering
1The University of Texas at Arlington · 2Amazon
Fursee: Hybrid YOLO-DINOv3 Framework for Fursuit Identity Retrieval and Clustering
Jun 22, 2026Jundi WuVision Foundation Models Image Matching
Shandong University Qingdao, China
Reducing Bias and Variance: Generative Semantic Guidance and Bi-Layer Ensemble for Image Clustering
May 13, 2026Feijiang Li, Zhenxiong Li, Jieting Wang +3Ensemble Learning Clustering
Institute of Big Data Science and Industry, Shanxi University · Key Laboratory of Evolutionary Science Intelligence of Shanxi Province · School of Artificial Intelligence, Shanxi University
Deep Sprite-based Image Models: An Analysis
Apr 21, 2026Zeynep Sonat Baltacı, Romain Loiseau, Mathieu AubryClustering Object-Centric Representation Learning
LIGM, CNRS, Univ Gustave Eiffel, ENPC, Institut Polytechnique de Paris, France
Room Scene Discovery and Grouping in Unstructured Vacation Rental Image Collections
Jun 30, 2025Vignesh Ram Nithin Kappagantula, Shayan HassantabarImage Clustering
Expedia Group Seattle, Washington, USA

Oct 7, 2026, cs.CVJ/K move · Enter open · S save

LoomSC: Scalable Deep Subspace Clustering with Projector Factorization and Exact Spectral Reduction

Nairouz Mrabah, Youssef Melki, Mohamed Bouguessa, Riadh Ksantini+2

École de technologie supérieure (ÉTS), Montréal, QC, Canada · University of Bahrain (UOB), Kingdom of Bahrain · Université du Québec à Montréal (UQAM), Montréal, QC, Canada+1

Clustering Spectral Clustering Deep Clustering Image Clustering Orthogonal Representation Learning

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Dense self-expression matrices and full-affinity spectral clustering limit the scalability of subspace clustering. We introduce the Latent Orthogonal Optimization Model for Subspace Clustering (LoomSC), a framework that addresses both bottlenecks through projector factorization and exact spectral reduction. Motivated by the spectral structure of least-squares regression, LoomSC jointly learns latent features and a projector self-representation through two thin factors. Alternating Procrustes and least-squares updates preserve the sample factor's orthogonality while keeping the coefficient matrix implicit. We construct a nonnegative quadratic affinity that preserves the projector's support. An exact feature map then reduces its normalized spectral problem to an eigenproblem whose dimension depends only on the factor width. Neither the full affinity nor the sample Laplacian needs to be formed. Our analysis quantifies the projector approximation and identifies conditions for subspace preservation and within-subspace connectivity. For fixed dimensions and iteration budgets, the complete pipeline has linear time and memory complexity in the number of samples. Across five image-clustering benchmarks, LoomSC ranks first or second in all 15 dataset-metric comparisons against 9 state-of-the-art baselines. Its mean accuracy exceeds the highest baseline mean by 6.66 percentage points. Synthetic experiments scale to 500,000 samples while maintaining at least 99.8% accuracy.

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