Recognition of Geometrical Shapes by Dictionary Learning
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915243735646208 |
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| author | Köhler, Alexander Breuß, Michael |
| author_facet | Köhler, Alexander Breuß, Michael |
| contents | Dictionary learning is a versatile method to produce an overcomplete set of vectors, called atoms, to represent a given input with only a few atoms. In the literature, it has been used primarily for tasks that explore its powerful representation capabilities, such as for image reconstruction. In this work, we present a first approach to make dictionary learning work for shape recognition, considering specifically geometrical shapes. As we demonstrate, the choice of the underlying optimization method has a significant impact on recognition quality. Experimental results confirm that dictionary learning may be an interesting method for shape recognition tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10958 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Recognition of Geometrical Shapes by Dictionary Learning Köhler, Alexander Breuß, Michael Computer Vision and Pattern Recognition Machine Learning 00A69, 68U05, 68T05 Dictionary learning is a versatile method to produce an overcomplete set of vectors, called atoms, to represent a given input with only a few atoms. In the literature, it has been used primarily for tasks that explore its powerful representation capabilities, such as for image reconstruction. In this work, we present a first approach to make dictionary learning work for shape recognition, considering specifically geometrical shapes. As we demonstrate, the choice of the underlying optimization method has a significant impact on recognition quality. Experimental results confirm that dictionary learning may be an interesting method for shape recognition tasks. |
| title | Recognition of Geometrical Shapes by Dictionary Learning |
| topic | Computer Vision and Pattern Recognition Machine Learning 00A69, 68U05, 68T05 |
| url | https://arxiv.org/abs/2504.10958 |