Non-Parametric Clustering Using Deep Neural Networks
In this paper, a novel algorithm for non-parametric image clustering, is proposed. Non-parametric clustering methods operate by considering the number of clusters unknown as opposed to parametric clustering, where the number of clusters is known a priori. In the present work, a deep neural network i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9171232/ |
| _version_ | 1818557445847384064 |
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| author | Christos Avgerinos Vassilios Solachidis Nicholas Vretos Petros Daras |
| author_facet | Christos Avgerinos Vassilios Solachidis Nicholas Vretos Petros Daras |
| author_sort | Christos Avgerinos |
| collection | DOAJ |
| description | In this paper, a novel algorithm for non-parametric image clustering, is proposed. Non-parametric clustering methods operate by considering the number of clusters unknown as opposed to parametric clustering, where the number of clusters is known a priori. In the present work, a deep neural network is trained, in order to decide whether an arbitrary sized group of elements can be considered as a unique cluster or it consists of more than one clusters. Using this trained neural network as clustering criterion, an iterative algorithm is built, able to cluster any given dataset. Evaluation of the proposed method on several public datasets shows that the proposed method is either on par or outperforms state-of-the-art methods even when compared to parametric image clustering methods. The proposed method is additionally able to correctly cluster input samples from a completely different dataset than the one it has been trained on, as well as data coming from different modalities. Results on cross-dataset clustering show evidence of the generalization potential of the proposed method. |
| first_indexed | 2024-12-13T23:59:35Z |
| format | Article |
| id | doaj.art-34fe660767a24e209f990c16a424513e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T23:59:35Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-34fe660767a24e209f990c16a424513e2022-12-21T23:26:23ZengIEEEIEEE Access2169-35362020-01-01815363015364010.1109/ACCESS.2020.30178849171232Non-Parametric Clustering Using Deep Neural NetworksChristos Avgerinos0https://orcid.org/0000-0003-1633-3857Vassilios Solachidis1https://orcid.org/0000-0002-0761-5396Nicholas Vretos2https://orcid.org/0000-0003-3604-9685Petros Daras3https://orcid.org/0000-0003-3814-6710Visual Computing Laboratory, Centre for Research and Technology Hellas-Information Technologies Institute, Thessaloniki, GreeceVisual Computing Laboratory, Centre for Research and Technology Hellas-Information Technologies Institute, Thessaloniki, GreeceVisual Computing Laboratory, Centre for Research and Technology Hellas-Information Technologies Institute, Thessaloniki, GreeceVisual Computing Laboratory, Centre for Research and Technology Hellas-Information Technologies Institute, Thessaloniki, GreeceIn this paper, a novel algorithm for non-parametric image clustering, is proposed. Non-parametric clustering methods operate by considering the number of clusters unknown as opposed to parametric clustering, where the number of clusters is known a priori. In the present work, a deep neural network is trained, in order to decide whether an arbitrary sized group of elements can be considered as a unique cluster or it consists of more than one clusters. Using this trained neural network as clustering criterion, an iterative algorithm is built, able to cluster any given dataset. Evaluation of the proposed method on several public datasets shows that the proposed method is either on par or outperforms state-of-the-art methods even when compared to parametric image clustering methods. The proposed method is additionally able to correctly cluster input samples from a completely different dataset than the one it has been trained on, as well as data coming from different modalities. Results on cross-dataset clustering show evidence of the generalization potential of the proposed method.https://ieeexplore.ieee.org/document/9171232/Cross-datasethigh dimensional clusteringmachine learningnon-parametric |
| spellingShingle | Christos Avgerinos Vassilios Solachidis Nicholas Vretos Petros Daras Non-Parametric Clustering Using Deep Neural Networks IEEE Access Cross-dataset high dimensional clustering machine learning non-parametric |
| title | Non-Parametric Clustering Using Deep Neural Networks |
| title_full | Non-Parametric Clustering Using Deep Neural Networks |
| title_fullStr | Non-Parametric Clustering Using Deep Neural Networks |
| title_full_unstemmed | Non-Parametric Clustering Using Deep Neural Networks |
| title_short | Non-Parametric Clustering Using Deep Neural Networks |
| title_sort | non parametric clustering using deep neural networks |
| topic | Cross-dataset high dimensional clustering machine learning non-parametric |
| url | https://ieeexplore.ieee.org/document/9171232/ |
| work_keys_str_mv | AT christosavgerinos nonparametricclusteringusingdeepneuralnetworks AT vassiliossolachidis nonparametricclusteringusingdeepneuralnetworks AT nicholasvretos nonparametricclusteringusingdeepneuralnetworks AT petrosdaras nonparametricclusteringusingdeepneuralnetworks |