Visual Motif Identification: Elaboration of a Curated Comparative Dataset and Classification Methods
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912079674343424 |
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| author | Phillips, Adam Rodriguez, Daniel Grandes Sánchez-Manzano, Miriam Salvadó, Alan Garin, Manuel Haro, Gloria Ballester, Coloma |
| author_facet | Phillips, Adam Rodriguez, Daniel Grandes Sánchez-Manzano, Miriam Salvadó, Alan Garin, Manuel Haro, Gloria Ballester, Coloma |
| contents | In cinema, visual motifs are recurrent iconographic compositions that carry artistic or aesthetic significance. Their use throughout the history of visual arts and media is interesting to researchers and filmmakers alike. Our goal in this work is to recognise and classify these motifs by proposing a new machine learning model that uses a custom dataset to that end. We show how features extracted from a CLIP model can be leveraged by using a shallow network and an appropriate loss to classify images into 20 different motifs, with surprisingly good results: an $F_1$-score of 0.91 on our test set. We also present several ablation studies justifying the input features, architecture and hyperparameters used. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15866 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Visual Motif Identification: Elaboration of a Curated Comparative Dataset and Classification Methods Phillips, Adam Rodriguez, Daniel Grandes Sánchez-Manzano, Miriam Salvadó, Alan Garin, Manuel Haro, Gloria Ballester, Coloma Computer Vision and Pattern Recognition I.4.9; J.5 In cinema, visual motifs are recurrent iconographic compositions that carry artistic or aesthetic significance. Their use throughout the history of visual arts and media is interesting to researchers and filmmakers alike. Our goal in this work is to recognise and classify these motifs by proposing a new machine learning model that uses a custom dataset to that end. We show how features extracted from a CLIP model can be leveraged by using a shallow network and an appropriate loss to classify images into 20 different motifs, with surprisingly good results: an $F_1$-score of 0.91 on our test set. We also present several ablation studies justifying the input features, architecture and hyperparameters used. |
| title | Visual Motif Identification: Elaboration of a Curated Comparative Dataset and Classification Methods |
| topic | Computer Vision and Pattern Recognition I.4.9; J.5 |
| url | https://arxiv.org/abs/2410.15866 |