Style-based Clustering of Visual Artworks and the Play of Neural Style-Representations
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916594350817280 |
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| author | Dangeti, Abhishek Gajula, Pavan Srivastava, Vivek Jamwal, Vikram |
| author_facet | Dangeti, Abhishek Gajula, Pavan Srivastava, Vivek Jamwal, Vikram |
| contents | Clustering artworks based on style can have many potential real-world applications like art recommendations, style-based search and retrieval, and the study of artistic style evolution of an artist or in an artwork corpus. We introduce and deliberate over the notion of 'Style-based clustering of visual artworks'. We argue that clustering artworks based on style is largely an unaddressed problem. We explore and devise different neural feature representations - from the style-classification, style-transfer to large language vision models - that can be then used for style-based clustering. Our objective is to assess the relative effectiveness of these devised style-based clustering approaches through qualitative and quantitative analysis by applying them to multiple artwork corpora and curated synthetically styled datasets. Besides providing a broad framework for style-based clustering and evaluation, our analysis provides some key novel insights on feature representations, architectures and implications for style-based clustering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08245 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Style-based Clustering of Visual Artworks and the Play of Neural Style-Representations Dangeti, Abhishek Gajula, Pavan Srivastava, Vivek Jamwal, Vikram Computer Vision and Pattern Recognition Machine Learning I.4.8; I.5.3 Clustering artworks based on style can have many potential real-world applications like art recommendations, style-based search and retrieval, and the study of artistic style evolution of an artist or in an artwork corpus. We introduce and deliberate over the notion of 'Style-based clustering of visual artworks'. We argue that clustering artworks based on style is largely an unaddressed problem. We explore and devise different neural feature representations - from the style-classification, style-transfer to large language vision models - that can be then used for style-based clustering. Our objective is to assess the relative effectiveness of these devised style-based clustering approaches through qualitative and quantitative analysis by applying them to multiple artwork corpora and curated synthetically styled datasets. Besides providing a broad framework for style-based clustering and evaluation, our analysis provides some key novel insights on feature representations, architectures and implications for style-based clustering. |
| title | Style-based Clustering of Visual Artworks and the Play of Neural Style-Representations |
| topic | Computer Vision and Pattern Recognition Machine Learning I.4.8; I.5.3 |
| url | https://arxiv.org/abs/2409.08245 |