Resolving Stylistic Overlap in Art Movements Using Semantic Embedding and Uniform Manifold Approximation and Projection

Resolving Stylistic Overlap in Art Movements Using Semantic Embedding and Uniform Manifold Approximation and Projection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Resolving Stylistic Overlap in Art Movements Using Semantic Embedding and Uniform Manifold Approximation and Projection Rekha Sharma, Rishi Gupta, Aditya Sinha, Abhay Bisht This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7870677/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The growing availability of large-scale digitized fine art collections has opened new avenues for research in computational art analysis. Among these, accurately identifying the art movement of a painting is crucial for tasks such as indexing vast art databases. However, this task is complicated by the significant stylistic overlap between various art movements. This generated a lower F1 score in multiclass classification. According to our observation, there is a lack of High-Level Features (HLF) that capture the semantic information of the paintings. In this work we have proposed semantic analysis as a High level features. To obtain HLF, we employed a novel methodology based on a pipeline utilizing the Visual Language Model (VLM), Sentence Transformer, and Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP). While classifying art movements (Abstract Expressionism, Color_Field_Painting, Early_Renaissance, Expressionism, Impressionism, Minimalism, and Realism) from the WikiArt dataset, we achieved a 98% recognition rate in Impressionism, which is a significant improvement over state-of-the-art classification models and achieved 75% overall accuracy, in the recognition of art movements. Visual Language Model Uniform Manifold Approximation and Projection for Dimension Reduction Feature extraction Semantic Feedforward neural network Art movement recognition Industry Innovation. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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