METHOD AND SYSTEM FOR STYLE BASED CLUSTERING OF ARTWORKS WITH NATURAL LANGUAGE STYLE ANNOTATIONS

원문 (중국어)

This disclosure relates generally to a system and method for style-based clustering of artworks with natural language style annotations. The conventional methods generate generic image feature representations derived from deep neural networks and do not specifically deal with the artistic style. The present disclosure, generates style-based artwork representations based on caption with style-based keywords and style concept annotations by leveraging image captioning model, vision language model and text encoder. Further style-based latent feature representations are generated from the style-based artwork representations for performing unsupervised clustering. The clustering of style-based latent feature representations is done based on deep embedded clustering using dynamic or static initialization of clusters. The present disclosure helps in discovering finer-grained style concepts within a corpus of artwork in an unsupervised manner. It also helps explore and create the art style evolution-based narratives and curative practices.