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.
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