Deep density-based image clustering

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 i...

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Vydané v:Knowledge-based systems Ročník 197; s. 105841
Hlavní autori: Ren, Yazhou, Wang, Ni, Li, Mingxia, Xu, Zenglin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier B.V 07.06.2020
Elsevier Science Ltd
Predmet:
Algorithms
Artificial neural networks
Clustering
Data
Deep clustering
Density
Density-based clustering
Experiments
Feature extraction
Feature learning
Machine learning
Neural networks
Task performance
Feature learning
Density-based clustering
Deep clustering
ISSN:0950-7051, 1872-7409
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Shrnutí: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. In the second stage, we propose a novel density-based clustering technique for 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.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2020.105841