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Authors: Salvatore Giurato ; Alessandro Ortis and Sebastiano Battiato

Affiliation: Image Processing Laboratory, Dipartimento di Matematica e Informatica, Universita’ degli Studi di Catania, Viale A. Doria 6, Catania - 95125, Italy

Keyword(s): Unsupervised Clustering, Image Clustering, Cos-Similarity, Stochastic Process, Batching.

Abstract: Traditional methods for unsupervised image clustering such as K-means, Gaussian Mixture Models (GMM), and Spectral Clustering (SC) have been proposed. However, these strategies may be time-consuming and labor-intensive, particularly when dealing with a vast quantity of unlabeled images. Recent studies have proposed incorporating deep learning techniques to improve upon these classic models. In this paper, we propose an approach that addresses the limitations of these prior methods by allowing for the association of multiple images at a time to each group and by considering images that are extremely close to the images that are already associated to the correct cluster. Additionally, we propose a method for reducing and unifying clusters when the number of clusters is deemed too high by the user, utilizing four different heuristics while considering the clustering as a single element. Our proposed method is able to analyze and group images in real-time without any prior training. Expe riments confirm the effectiveness of the proposed strategy in various setting and scenarios. (More)


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Paper citation in several formats:
Giurato, S.; Ortis, A. and Battiato, S. (2023). FUB-Clustering: Fully Unsupervised Batch Clustering. In Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - IMPROVE; ISBN 978-989-758-642-2; ISSN 2795-4943, SciTePress, pages 156-163. DOI: 10.5220/0011969500003497

author={Salvatore Giurato. and Alessandro Ortis. and Sebastiano Battiato.},
title={FUB-Clustering: Fully Unsupervised Batch Clustering},
booktitle={Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - IMPROVE},


JO - Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - IMPROVE
TI - FUB-Clustering: Fully Unsupervised Batch Clustering
SN - 978-989-758-642-2
IS - 2795-4943
AU - Giurato, S.
AU - Ortis, A.
AU - Battiato, S.
PY - 2023
SP - 156
EP - 163
DO - 10.5220/0011969500003497
PB - SciTePress