On the Effectiveness of Heterogeneous Ensemble Methods for Re-identification
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911803480473600 |
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| author | Klüttermann, Simon Rutinowski, Jérôme Nguyen, Anh Grimme, Britta Roidl, Moritz Müller, Emmanuel |
| author_facet | Klüttermann, Simon Rutinowski, Jérôme Nguyen, Anh Grimme, Britta Roidl, Moritz Müller, Emmanuel |
| contents | In this contribution, we introduce a novel ensemble method for the re-identification of industrial entities, using images of chipwood pallets and galvanized metal plates as dataset examples. Our algorithms replace commonly used, complex siamese neural networks with an ensemble of simplified, rudimentary models, providing wider applicability, especially in hardware-restricted scenarios. Each ensemble sub-model uses different types of extracted features of the given data as its input, allowing for the creation of effective ensembles in a fraction of the training duration needed for more complex state-of-the-art models. We reach state-of-the-art performance at our task, with a Rank-1 accuracy of over 77% and a Rank-10 accuracy of over 99%, and introduce five distinct feature extraction approaches, and study their combination using different ensemble methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12606 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On the Effectiveness of Heterogeneous Ensemble Methods for Re-identification Klüttermann, Simon Rutinowski, Jérôme Nguyen, Anh Grimme, Britta Roidl, Moritz Müller, Emmanuel Machine Learning In this contribution, we introduce a novel ensemble method for the re-identification of industrial entities, using images of chipwood pallets and galvanized metal plates as dataset examples. Our algorithms replace commonly used, complex siamese neural networks with an ensemble of simplified, rudimentary models, providing wider applicability, especially in hardware-restricted scenarios. Each ensemble sub-model uses different types of extracted features of the given data as its input, allowing for the creation of effective ensembles in a fraction of the training duration needed for more complex state-of-the-art models. We reach state-of-the-art performance at our task, with a Rank-1 accuracy of over 77% and a Rank-10 accuracy of over 99%, and introduce five distinct feature extraction approaches, and study their combination using different ensemble methods. |
| title | On the Effectiveness of Heterogeneous Ensemble Methods for Re-identification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2403.12606 |